Utilitarian Value in the Internet: Differences Between Broadband and Narrowband Users Srikanth Beldona Sheryl F. Kline Alastair M. Morrison
SUMMARY. As homes across the world adopt broadband connectivity, there is a need to understand how it may impact consumer propensity to buy travel products on the Internet. The objective of this study is to evaluate differences in perceptions of utilitarian and social value on the Internet between broadband and narrowband users. The study also explores the relationship of utilitarian and social value on the Internet within the context of online travel purchase behavior. MANOVA results indicate differences between broadband and narrowband users when it comes to self-improvement and functional dimensions of utilitarian value. Practical and theoretical implications are also discussed. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: Website: © 2004 by The Haworth Press, Inc. All rights reserved.]
KEYWORDS. Perceived Internet value, online consumer behavior, technology adoption, broadband
INTRODUCTION Broadband is considered to be a wide or high bandwidth medium that offers fast transmission speeds for high-volume data applications typically on the Internet. Home broadband penetration in the United States now stands at 26%, with another 29% planning to get broadband in the next two years (Kolko, Mcquivey, Gordon & Strohm, 2003). Broadband connectivity al-
lows for the use of richer applications and an enhanced Internet experience compared to narrowband connectivity that works using a dial-up modem with limited bandwidth. A typical broadband customer has been categorized as progressive, success oriented, and ahead in the technology adoption curve (Jackson, Montigni, & Pearce, 2001). Significantly, research has indicated that broadband users show a greater likelihood of buying online (33%) com-
Srikanth Beldona is Assistant Professor, Department of Nutrition and Hospitality Management, College of Human Ecology, East Carolina University. Sheryl F. Kline is Assistant Professor, Department of Hospitality & Tourism Management, School of Consumer and Family Sciences, Purdue University. Alastair M. Morrison is Associate Dean, School of Consumer and Family Sciences, Purdue University. Address correspondence to: Srikanth Beldona (E-mail:
[email protected]). [Haworth co-indexing entry note]: “Utilitarian Value in the Internet: Differences Between Broadband and Narrowband Users.” Beldona, Srikanth, Sheryl F. Kline, and Alastair M. Morrison. Co-published simultaneously in Journal of Travel & Tourism Marketing (The Haworth Hospitality Press, an imprint of The Haworth Press, Inc.) Vol. 17, No. 2/3, 2004, pp. 63-77; and: Handbook of Consumer Behavior, Tourism, and the Internet (ed: Juline E. Mills, and Rob Law) The Haworth Hospitality Press, an imprint of The Haworth Press, Inc., 2004, pp. 63-77. Single or multiple copies of this article are available for a fee from The Haworth Document Delivery Service [1-800-HAWORTH, 9:00 a.m. - 5:00 p.m. (EST). E-mail address:
[email protected]].
http://www.haworthpress.com/web/JTTM 2004 by The Haworth Press, Inc. All rights reserved. Digital Object Identifier: 10.1300/J073v17n02_06
63
64
HANDBOOK OF CONSUMER BEHAVIOR, TOURISM, AND THE INTERNET
pared to narrowband users (Yonish, Delhagen & Gordon, 2002). Broadband appeal has catapulted the Internet to a medium on par with television and radio, when it comes to time spent by consumers. In a typical broadband home, time spent on the Internet is equal to that of television and closely followed by radio (Bouvard & Kurtzmann, 2002). This growth of broadband as a medium on par with television and radio is highlighting a paradigm shift in media consumption behavior. Other Internet consumption patterns also serve as key facts for input for future strategies. Compared to narrowband users (dial-up modem connection), broadband users are nearly four times as likely to download videos, 3.6 times likely to watch streaming videocasts and 3.07 times likely to listen to audiocasts (Kolko et al., 2003). At a very broad level, participation in the Internet has been distilled into two distinct perspectives namely: utilitarian and social (Chung & Henderson, 2001). In the utilitarian perspective, the Internet is primarily viewed as an efficient marketplace as well as a convenient source of information. From the social perspective, the Internet is seen primarily as a conduit of communication that facilitates social interaction. These two perspectives have become increasingly important given the changing landscape of Internet connectivity in American homes from narrowband to broadband. However, except for findings mentioned in the trade press, there is no evidence of any literature that probes deeper or explains the nuances of these behavioral perspectives amongst broadband users. Value in the Internet can be derived from the varying reasons it is used for, an aspect reflective of its multifaceted nature. Apart from being used as an information repository and a communication tool, it is used as a marketplace and is also considered a social system (Maignan & Lukas, 1997). Each of these dimensions of Internet usage can induce value perceptions that can serve as antecedents of purchase behavior (Beldona, Morrison & Ismail, 2003). Arguably broadband users may perceive greater value in the Internet over narrowband users. Greater value in the medium indicates greater ease of use, which in turn explains the rela-
tively greater propensities to buy amongst broadband users. The purpose of this paper therefore is to evaluate differences in perceived value in the Internet between broadband and narrowband users. Specifically, it derives utilitarian dimensions of perceived Internet value to explain differences between broadband and narrowband users. This is done based on a synthesis of literature spread across consumer behavior, social psychology and e-commerce. Identifying differences between these two groups (broadband and narrowband users) can help in developing a greater understanding of the drivers of online travel purchase behavior. This is all the more timely, because broadband is gradually altering media consumption behavior amongst consumers. From a theoretical standpoint, the study bridges a gap by bringing perceived value into the Internet research domain to explain behavioral differences between two groups at varying points in the technology adoption cycle. Growth in broadband usage has significant implications for the travel industry particularly with the steady growth of complex travel products such as cruises, vacations, and destinations (NYU/PhocusWright Report, 2003). Complex travel products are highly intangible, and demand higher involvement in search because of the depth and breadth of information they have to provide when compared with the relatively straight-forward booking of flights, hotels, and car rentals (Goossens, 1994; Vogt & Fesenmaier, 1998). Customers build expectations of their prospective travel experience based on available information (Moutinho, 1987; Fodness & Murray, 1997; Vogt & Fesenmaier, 1998). In the traditional context, travel marketers used brochures and videos to enable customers to gain perceptions of what they may expect from their future travel experience. However, the travel product is highly intangible, and customers make many decisions under conditions of uncertainty (Goossens, 1994; Vogt & Fesenmaier, 1998). It is here that travel marketers can enhance the quality of information provided through the use of broadband applications. Currently, the Internet is one of the most favorable information sources for travel, second only to “reference information” (Kolko et al., 2003). Web
Section 2: Travel Website User Characteristics
based multimedia applications such as flash, streaming video, virtual tours may go a long way to improve the information context of the travel decision making process. Practically speaking, the findings of this study may help online travel marketers to proactively align strategies in concordance with the changing landscape of media consumption habits. REVIEW OF LITERATURE Broadband and Narrowband A narrowband connection is accessed using a dial-up modem that is itself connected to the traditional copper wired telephone lines. Much of the growth of the Internet in the early to mid-’90s took place on this underlying infrastructure of telephone lines, a factor that helped in the rapid penetration of the Internet. Typically, narrowband connections transport data at speeds of 56 kilobytes per second or less (Paul, 2003). With Internet growth, the demand for richer forms of data such as video, audio, and rich images paved the way for technologies that enabled faster channels of access. Thereon, the Telecommunications Act of 1996 was introduced in the US, which provided a framework to encourage both competition and the development of advanced telecommunications services such as broadband. The Act enabled open access to telecommunications networks for consumers and service providers to leverage and enable the advent of futuristic services. A direct and immediate result of this act was the introduction of digital subscriber line technology (DSL) by telecommunication carriers. DSL is a modem technology that uses existing twisted-pair telephone lines to transport high-bandwidth data, such as multimedia and video (Online Cisco Manual, 2003). The advantage of DSL is its ability to still leverage the existing telephone infrastructure, while simultaneously providing higher speed Internet access compared to narrowband connections (Paul, 2003). Since traditional copper wires are capable of handling much greater bandwidth (range of frequencies) DSL exploits this “extra capacity” to carry information on the wire by matching particular frequencies to
65
specific tasks (Franklin, 2003). There are several types and variations of DSL, although Asymmetric Digital Subscriber Line (ADSL) is the most prominent and widely used (Paul, 2003; Franklin, 2003). This is largely because typical home users tend to receive or download much more information than they actually send. ADSL suits this usage profile (Paul, 2003). ADSL divides up the available frequencies in a telephone line in a manner where the connection speed from the Internet to the user is three to four times faster than the connection from the user back to the Internet (Franklin, 2003). Cable has also been the other alternative medium for accessing high speed Internet. This is done by leveraging existing TV cable lines provided by cable companies. Customers can use a cable modem to regulate the Internet connection at their end. Cable modem connections are comparable with DSL in terms of data transmission speeds. The actual cable wire is called a co-axial cable, where signals from the various channels are each given a 6-Megahertz slice of the cable’s available bandwidth (Franklin, 2003). One co-axial cable can carry hundreds of megahertz of signals. Like any Cable TV channel, the Internet is also provided on this cable through a 6 Megahertz channel, although upstream Internet (for uploads) has a 2MHz slice. Cable companies were also pioneers in providing interactive services largely because of their first mover advantage (Paul, 2003). In the US, cable outperforms DSL with nearly twice as many subscribers. There are other technologies that also fall under the heading of broadband namely T1 and T3. A T1 line is typically delivered by phone companies, has a fiber optic line (although copper wires are also used) and has data transmission speeds of 1.5 Megabits per second that is faster than DSL or cable (Brian, 2003; Campus-Wide Information Systems, 2001). As for T3, it is a high-speed connection capable of transmitting data at rates of up to 45 Mbps, and is comprised of 28 T1 lines (Brian, 2003). According to Paul (2003), electric companies are vying to provide broadband access using existing electrical wires, a fact that may further enhance broadband penetration in the US.
66
HANDBOOK OF CONSUMER BEHAVIOR, TOURISM, AND THE INTERNET
Broad and Narrowband Technologies and Online Travel Purchasing There have been numerous studies that evaluate the predictors of online purchasing as a whole (Bellman, Lohse, & Johnson, 1999; Swaminathan, Lepkowska-White & Rao, 1999). Specific to online travel purchasing, a few studies have evaluated the predictors of purchase using behavioral and demographic variables (e.g., Beldona, Morrison, & Ismail, 2003; Morrison, Su, O’Leary & Cai, 2001; Weber & Roehl, 1999; Bonn, Furr & Susskind, 1998). However, there is no evidence of any study that explains the consumer’s individual attachment to the underlying technology and evaluates this relationship within the context of online travel purchasing behavior. Trade press research has highlighted broadband users to have a success-oriented outlook. They have demonstrated greater propensities to buy as well as adopt innovative technologies or business models that have emerged on the Web (Yonish et al., 2002). Broadband services enable richer functionality of applications for communication. For example, operating Webcams or using virtual tours are far more effective with broadband connectivity because of faster data speeds that result in richer quality of images. Interestingly, the use of these technologies in travel Websites has not increased the likelihood of purchase at travel or destination Websites (Forrester Report, 2002). Multimedia capabilities of the Internet can have a significant impact on the marketing of travel products (Cho, Wong & Fesenmaier, 2002). For example, operating Webcams or using virtual tours are far more effective with broadband connectivity because of faster data speeds that result in richer quality of images. Economics of information theory (Nelson, 1970; Darby & Karni, 1973) categorized products into search, experience and credence types based on how consumers evaluate them. Products with search qualities can be fully evaluated prior to purchase, whereas experience based products must be first purchased and consumed before the consumer is able to evaluate. Darby and Karni (1973) extended this to include credence goods, which consumers can never fully evaluate even after pur-
chase and consumption. Zeithaml (1981) integrated this categorization to marketing, and posited that services exhibit more experience and credence qualities due to their unique characteristics of intangibility, non-standardization and inseparability. Bringing the realm of travel products within this search-experience-credence framework categorization provides cues on the nature of search and purchase on the online medium–especially, given that the Internet is an interactive tool that works in a computer mediated environment, and is able to provide greater detail on features of products sold using comparison charts in text form, virtual tours, video and still image formats. Klein (1998) contends that the Internet’s rich interactivity and content can push experience based products into the searchable domain. For example, flights, accommodations and car rentals are standardized services that can be placed within the easier to evaluate context as there are more known parameters of tangibility (Zeithaml, 1981; Mittal, 1999). In contrast, complex travel products such as cruises, land based vacations, tours, activities and attractions can be arguably placed in the difficult to evaluate context. It is with complex travel products that broadband technologies can play a significant role in improving searchable qualities. Environmental simulations such as sketches, photographs, and video are imperative towards communicating the image of a destination (Cho, Wang & Fesenmaeier, 2002). For example, streaming video can give detail on tours and activities at destinations, as to where and when they start. Webcams can also provide real time glimpses of the destination, thereby increasing excitement value and providing a “feel” of the destination. Tools such as these can greatly enhance the reliability and effectiveness of marketing communication. Incidentally, the use of rich video and related broadband application also tend to improve the experiential elements of travel information search, which in turn can impact the sensory and aesthetic needs (Vogt & Fesenmaier, 1998). Many resort hotels already provide virtual tours of facilities and accommodations. The real estate industry has used applications suited for broadband for a long time. Cho et al. (2002) expand on virtual
Section 2: Travel Website User Characteristics
tours in the travel context using the concept of virtual experiences. Virtual experiences provide both direct and indirect experiences to enable travelers to better anticipate the nuances of the prospective experience (Daugherty, Li & Biocca, 2001). Direct experience comprises aspects such as trial or inspection whereas the use of brochures or advertising implies indirect experiences (Hoch & Deighton, 1989). Holbrook and Hirschmann (1982) proposed the experiential view of consumer choice that accommodates for the experience of the search itself. The experiential view includes consumer fantasies, imagination, affect and subjective reactions to consumption experiences that have an important influence on their behavior. Holbrook and Hirschman (1982), posited that leisure activities, aesthetic goods, and their like entail a different decision-making process from that suggested by the information-processing paradigm. Schmitt (1999) confirmed this perception of the experiential view of consumer decision making as key to representation of consumer choice than the more traditional models for all types of products. Extending Hirschmann and Holbrook’s (1982) experiential view into travel literature, Vogt and Fesenmaier (1998) stretched the functional travel information search framework to include four other dimensions, namely sign, hedonic, innovation and aesthetic needs. While the sign dimension symbolized the search for expression and social interaction, the hedonic aspect reflected the experiential contexts of emotion, sensory needs and phenomenology. They also found that consumers may seek innovation from travel information search as well as try and satisfy aesthetic needs as well. The five dimensions of search including the functional component were structured around travel information sources that were categorized as social, personal, marketing and editorial. The Internet Medium and Technology Adoption The Internet as a marketplace has been extensively researched illustrating the efficacy of the Internet as a medium for market exchange activities (Bakos, 1998). However, the
67
Internet is also a vast information resource that serves as a key tool for improving the overall perceived productivity of individuals using it. From finding information pertaining to a particular health condition, to seeking key facts that help solve a problem with one’s automobile, the Internet is increasingly becoming a valuable resource. In many ways, these issues highlight a greater perception of usefulness or value in the Internet as a tool in important activities. Greater reliability in the medium results in improved overall acceptability for commerce or exchange. In a very holistic sense, these developments have an impact on travel purchasing behavior because they indicate a shift in user beliefs towards an evolving medium. One may contend that such developments enhance the “Do it yourself” attitude amongst consumers when it comes to buying travel online. However, technology adoption in this context is better viewed through the framework provided by the Technology Adoption Model (TAM) (Davis, 1989), that is considered to be most widely used model of IT adoption. According to TAM, IT adoption is influenced by two primary constructs, namely perceived usefulness and perceived ease-of-use, which determine an individual’s behavioral intention to use the technology (Davis, Bagozzi & Warshaw, 1989). While perceived usefulness is more extrinsic in nature and is task-oriented, perceived ease of use is more intrinsic that relates to ease of learning, flexibility, and clarity of its interface (Gefen & Straub, 2000). However, applying TAM to e-commerce adoption requires the incorporation of barriers of perceived risk, due to the inherent characteristics of online transactional systems (Gefen & Straub, 2000; Pavlou, 2003; Pavlou, 2002). This is because e-commerce stretches beyond the domain of traditional information systems and any application of TAM must be integrated with the theory of marketing exchange (Sheth & Parvatiyar, 1995; Dwyer, Schurr & Oh, 1987). Lee, Park and Ahn (2000) expanded on the Technology Acceptance Model (TAM) and introduced an e-Com adoption model that included perceived risk with products/services, and perceived risk in the context of online transaction in addition to the existing perceived usefulness and perceived ease of
68
HANDBOOK OF CONSUMER BEHAVIOR, TOURISM, AND THE INTERNET
use constructs. Salisbury, Pearson and Harrison (1998) found that the perceived usefulness of the Internet was a significant predictor of search, but not purchase. Perceived behavioral control and trust have also been acknowledged as key constructs in e-commerce adoption (Pavlou, 2002). Perceived behavioral control reflects beliefs pertinent to resources and opportunities required to facilitate behaviors that users may have (Ajzen, 1991). Trust on the other hand has been a key construct in the theory of marketing exchange (Hoffmann, Novak & Peralta, 1999). From a strictly marketing perspective, the technology adoption cycle has been used to categorize users based on beliefs and attitudes (Parasuraman and Colby, 2003). The technology adoption cycle states that when a technology is introduced in the market, its adoption stages are characterized by five stages, namely Explorers, Pioneers, Skeptics, Paranoids, and Laggards (Parasuraman and Colby, 2001). Each segment varies based on a combination of optimism, innovativeness, discomfort, and insecurity in attitudes towards the technology. For example, low-end laggards are low in optimism and innovativeness, and high in discomfort and insecurity. Each segment develops over time to become a viable customer group. The process is not exactly sequential, although the categorization provides a strong set of guidelines for customer segmentation. Marketing strategies should be built upon an understanding of this adoption classification, by tailoring services to match the specific behavioral attributes and needs of each segment (Parasuraman and Colby, 2001). The typical broadband customer has been categorized as an early adopter of technology. Studies have found significant association between demographics and attitudinal factors in the adoption of new technologies (Shimp and Beardon, 1982; Rogers, 1995; Dabholkar, 1996). Formative studies on Internet usage profiles suggest the importance of demographic factors such as education, race, and occupation are significant predictors of increased Web usage (Pitkow and Kehoe, 1996). These earlier studies found that Internet users were typically male and had higher incomes (Pitkow and Kehoe, 1996; Bonn, Furr and Susskind, 1998). This group can be typified as “explorers”
based upon Parasuraman and Colby’s (2001) customer segmentation of technology adoption. Subsequently, Bonn et al. (1998) found significant differences in age, education, and the level of Internet use between pleasure travelers who would seek travel information online and those who do not. Shopping experiences have been explained along two dimensions–utilitarian and hedonic (Babin, Darden & Griffin, 1994). This has been the case with the physical marketplace as well as the virtual marketspace that encapsulates the Internet as a shopping medium. When viewed from a consumption perspective, the Internet is akin to a shopping mall in a physical context, wherein there are features that eventually serve as catalysts to the purchase process. While a shopping mall may have parking, accessibility and good layout, that all build a shopping friendly environment, the Internet has a broader array of features that may largely depend on the users’ perceptions of how it is used. Users’ representations of the Internet as a source of information, communication tool, object/place of consumption, or social system (Maignan & Lukas, 1997) should be taken into account to understand their overall propensity to browse, buy or entertain themselves. This is based on the theory of reasoned action (Fishbein & Ajzen, 1975) which suggests that attitudes can be used to predict behavioral intentions and behaviors. Research on the Internet as an object of consumption or a marketplace is prevalent in both information systems (IS) and marketing literature. O’Cass (2001) examined issues related to Internet involvement, subjective knowledge, thinking-feeling and personal and materialistic values related to the Internet as an object to be consumed. However, this scale of perceived usefulness of the Internet was uni-dimensional and did not effectively capture the multifaceted nature of the Internet as a medium. Chung and Henderson (2001) viewed the Internet medium from a participation perspective, and identified utilitarian and social dimensions. They found that utilitarian and the social participation perspectives played important roles in acceptance and use of the Internet. Bourdeau, Chebat and Couturier (2002) took it a step further and found five dimensions of the Internet usage, namely utilitarian,
Section 2: Travel Website User Characteristics
social, hedonic, learning and purchasing. However, they evaluated these dimensions across Web and email users only, and did not evaluate propensity to purchase. The Internet as a medium for social interaction has also been studied extensively. Some have also termed the Internet as a “social technology” because of its ability to help connect with family and friends, as well as make new social connections (Kraut et al., 1998). Because of the continuous connectivity that is global in reach, there is potential to interact with multiple numbers of persons in differing geographical locations in real time. Technologies such as chat rooms and bulletin boards enable the development of new relationships and consequently expand one’s social circle (Yamauchi and Coget, 2002). RESEARCH METHODS Data Data used for the study were obtained from the March 2000 survey conducted by the Pew Internet Research Center. Of the 1,501 respondents only 862 reported themselves as Internet users. From this 862 set of Internet users, 715 cases qualified within the context of having either broadband or narrowband connections at home. The respondents for this study were 18 years of age or older, and the sample was randomly drawn from telephone exchanges across the continental United States. Random sampling was done by generating the last two digits of telephone numbers selected on the basis of their area code, telephone exchange, and bank number digit. This sample had representation of both listed and unlisted numbers (including not-yet-listed numbers). The survey was administered by telephone, and included both offline and online users. The dataset also included a weight variable which was derived from a demographic weighting procedure using parameters from the Current Population Survey, and balances for race and gender. Methodology and Analysis Of the total of 715 cases in the original dataset, 634 were categorized as narrowband
69
users and the remaining 81 as broadband users. This was based on a question posed to respondents that sought the type of Internet connection (dial up modem, T1, DSL, and Cable). With the exception of the dial-up modem connection type (narrowband), connections categorized as broadband were in compliance with the technological underpinning of a broadband connection (Paul, 2003). Before any analysis was performed, the 634 cases of narrowband users in the original dataset was reduced to 83 using a random selection of cases as provided by SPSS 11.0. SPSS 11.0 allows for random extraction of a percentage of cases from a dataset, and this procedure was used here. Final group sizes were 83 cases for narrowband and 81 cases representing broadband users. The purpose of the above exercise was to achieve a fair level of parity in the number of cases between the two groups. This was required since the objective of the study is to evaluate differences between the two groups. Also, MANOVA (Multivariate Analysis of Variance) analysis, done further in the study, requires as key assumption equal cell sizes (Garson, 2001). MANOVA tends to be more robust when cell sizes are equal or close to equal. In addition cell size parity reduces the likelihood of violating the unequal variance assumption (Garson, 2001). Henceforth, mention of the phrase “two groups” means broadband and narrowband users, unless stated otherwise. Statistical analysis for the study was undertaken in three stages. The first stage involved preliminary chi-square analysis to evaluate the differences of online travel purchase propensities between the two groups. This stage also involved descriptive statistics that explained for any differences between the two groups across a range of demographic variables. The second stage of the study involved the analysis of perceived Internet value dimensions using a principal components analysis (PCA). PCA was administered on the pooled number of cases (164) that included the 81 broadband and the 83 narrowband users. Items were drawn from questions that sought responses on how the Internet had improved respondents’ lives in ten areas. For example, one question was, “How much, if at all, has the
70
HANDBOOK OF CONSUMER BEHAVIOR, TOURISM, AND THE INTERNET
Internet improved your ability to shop?” Principal components analysis is a data reduction technique that is based on the evaluation of the total variability that an item (variable) has to offer (Garson, 2003). PCA was chosen because the objective was to reduce the number of items into a smaller set of components. This is important as the study is exploratory in nature and uses items from a secondary dataset. Therefore, principal factor analysis (PFA) was not considered as it is more appropriate for latent variable identification and subsequent scale development (Bryant & Yarnold, 1998). The third stage of the study involved the examination of differences in perceived Internet value between broadband and narrowband users. MANOVA was used to compare groups formed by categorical independent variables on group differences in a set of interval dependent variables (Garson, 2001). The dimensions identified from the PCA process served as the dependent variables, while the “type of Internet connection” (broadband and narrowband) was the independent variable. MANOVA was used as the items across each dimension captured different concepts, even though they were significantly correlated with each of the items across their respective dimensions. MANOVA also keeps Type I error rate down when dealing with multiple dependent variables that are somewhat correlated (Weinfurt, 1998). Lastly, separate MANOVA analyses were employed to evaluate differences across the three dimensions derived from principal components analysis between broadband and narrowband users. RESULTS AND DISCUSSION Results Descriptive statistics for key demographic variables across both broadband and narrowband users are illustrated in Table 1. The sample profile indicates that narrowband users are only marginally older (mean = 43.62) compared to broadband users (mean = 41.17), although no significant differences were apparent. There were no differences in gender composition between the two groups. A larger number of broadband users had family incomes
TABLE 1. Descriptive Statistics Broadband
Narrowband
F or 2
41.17 (12.50)
43.62 (13.36)
F: 1.447
Gender Male Female
45.7% 54.3%
53% 47%
c2: 0.217
Income < $50,000 > $50,000
18.5% 81.5%
45.8% 54.2%
c2: 13.93***
4.9% 13.6% 4.9%
0% 22.9% 3.6%
c2: 8.25
19.8%
25.3%
30.9% 25.9%
30.1% 18.1%
43.2% 56.8%
61.0% 39.0%
c2: 5.15*
19.8%
26.5%
c2: 1.05
80.2%
73.5%
34.6% 65.4%
56.6% 43.4%
Variables Age
Education High School Incomplete High School Grad Business, Tech, Vocational After School Some college/No 4 Yr Degree Bachelors' 4 Yr Degree Post Graduate First Went Online < 3 Yrs > 3 Yrs Online Travel Search Have Not Searched For Travel Information Searched For Travel Information Online Travel Purchase Have Not Bought Travel Bought Travel
c2: 8.03**
*p < 0.05, **p < 0.01, ***p < 0.001.
over $50,000 as compared with narrowband users. This significant difference is glaring because a larger number of narrowband users belonged to the under $50,000 bracket when compared with the alternative in their own group. Education levels appear to be similarly spread out between both groups with greater numbers having attended some college or attained 4-year degrees and above. When it comes to when they first went online, broadband users show greater experience levels having been online for 3 years or more compared to narrowband users. In the narrowband group, the “less than 3 years” group is overwhelmingly greater than the “more than 3 years” group amongst narrowband users. Chi-square tests of significance were administered to evaluate for differences between the broadband and narrowband users. Based on the cross tabulation matrix, conditional odds were computed to present the odds of buying travel of both broadband and narrowband users. Conditional odds are the odds of being in one category of a variable within another specific category of another variable (Rodgers, 1998). In our case here, the two variables are buying
Section 2: Travel Website User Characteristics
travel and the type of Internet connection, both of which have two categories each. Chi-square results indicate significant differences between broadband and narrowband users. Conditional odds that a broadband user picked at random will buy travel online is 1.89 compared to just 0.76 amongst narrowband users. Principal components analysis indicated three dimensions that explained 59.15% of the total variance. A varimax rotation was administered and three value dimensions emerged namely: utilitarian (self-improvement), utilitarian (functional), and social. The components and respective factor loadings of each item are mentioned in Table 2. Items that had loadings below 0.50, or loaded on both components were discarded from the final solution (Hair et al., 1987). One item namely “ability to learn new things” loaded on two dimensions and was discarded. Before pursuing any other analyses, Cronbach’s alpha was applied to test the reliability of the factor groupings. It is important to retain only those scales that report alphas above 0.60 for further analysis (Hair, Anderson, Tatham and Black, 1998). Utilitarian (self-improvement), and utilitarian (functional) reported alphas of 0.67 and 0.63 respectively and were retained. Since the alpha of the “social value” scale was 0.59, it was dropped from further analysis, and factor analysis was re-administered on the remaining selected items. The Kaiser-Meyer-Olkin measure of sampling adequacy and the Bartlett’s test of sphericity were evaluated to ascertain the viability of principal components analysis for the study. The Kaiser-Meyer-Olkin statistic was 0.77, wherein values between 0.7 and 0.8 are considered good (Hair et al., 1998; Hutcheson and Sofroniou, 1999, pp. 224-225). The Bartlett’s test was also significant (p < 0.001), therefore indicating that factor analysis was appropriate. From Table 2, the first factor grouping reflects the self-improvement aspect of utilitarian value. Items on this factor showed an almost extracurricular aspect of day-to-day life that is tied to self-improvement. In comparison, the second dimension had a functional and periodic aspect attached to each of the three items. The fact that the “ability to meet new people” item loaded on the first dimension, and not on the social dimension, can be
71
TABLE 2. Factors of Perceived Value in the Internet (N = 160) Factors (Reliability Alpha) Factor 1: Utilitarian (Self-Improvement) Value (0.67) ability to meet new people ability to find ways to deal with problems the way you get information about health care the way you pursue your hobbies or interests Factor 2: Utilitarian (Functional) Value (0.62) ability to do your job the way you manage your personal finances ability to shop Factor 3: Social Value (0.59) improve connections to your friends improve connections to members of your family
Loading
Eigen Value
Variance Explained
2.94
21.84%
1.22
13.77%
1.16
17.54%
0.75 0.74 0.70 0.50
0.81 0.69 0.66 0.79 0.84
Note 1: KMO Measure of Sampling Adequacy was 0.77 and Bartlett’s Test of Sphericity had an approx. chi-square value of 2447/64 and was significant at P < 0.001. Note 2: One item, namely “ability to learn new things,” was removed as it had cross-loadings across two factors at values above 0.40. Note 3: Social value was tested in MANOVA because the Cronbach Alpha reliability was below 0.60.
explained by the wording in the question. It can be argued that the word “ability” induces a more utilitarian context to the question. Another distinction is between the ability to find or create new relationships rather than to improve existing ones. While this scale was dropped from the analysis, there is some pertinent information in this context that can be used for future studies. Social value can also be viewed from a perspective of re-connecting with friends and family that may have become dormant. The relative wording of the “social value” questions that use the words “connection,” “friends,” etc., may have also contributed to the difference in the perception of questions when compared with those in the self-improvement dimension. Put simply, social value comprised the value perception in improving connections with family and friends. In the third and final stage of analysis, MANOVA was administered to evaluate differences in perceived Internet value between broadband and narrowband users (see Table 3). This was done on two derived dimensions of utilitarian value, namely self-improvement and functional. Social value was analyzed because of its low reliability alpha (0.59). In the test of assumptions, the Box’s M test statistic was found to be non-significant ruling out any
72
HANDBOOK OF CONSUMER BEHAVIOR, TOURISM, AND THE INTERNET
TABLE 3. Value Perceptions Across Broadband and Narrowband Users
TABLE 4. Multivariate and Univariate Effects Source
Items
Utilitarian the way you pursue your Value hobbies or interests (Selfthe way you get information Improvement) about health care ability to find ways to deal with problems ability to meet new people Utilitarian Value (Functional)
ability to do your job ability to shop the way you manage your personal finances
Multivariate df
F
159
2.68*
Univariate F
Broadband Mean
Narrowband Mean 2.18
the way you pursue your hobbies or interests
9.66**
2.72 2.27
2.02
ability to find ways to deal with problems
0.06
1.77
1.73
ability to meet new people
0.21 1.75
1.53
1.46
the way you get information about health care
2.42 2.56 2.05
2.30 2.04 1.72
Note: Respondents were asked to indicate their level of agreement on how the Internet had improved their lives along the above parameters (1: not at all; 2: only a little; 3: some; 4: a lot).
significant differences in the covariance matrices of cells in the factor design matrix. Also, the Levene test for both MANOVA models showed non-significant differences between all other cells within each model thereby ruling out any violation of the unequal variances assumption. To evaluate the significance of the models, Pillai’s trace was the chosen statistic. Pillai’s trace is more robust than the other significance tests namely Wilks’ Lambda and Hotelling’s trace (Olson, 1976). MANOVA results indicate significant differences between broadband and narrowband users in how they perceive utilitarian (self-improvement) value in the Internet. See Table 4 for MANOVA results that indicate both multivariate and univariate findings. Multivariate results indicate that broadband users perceive greater self-improvement utilitarian value compared to narrowband users. Except for the perceived value in the ability to pursue hobbies, univariate results show non-significance of all other items in this dimension. In the case of functional utilitarian value, the significant multivariate F in the model indicates overall differences between broadband and narrowband users. Mean differences between the two groups indicate that broadband users perceive greater value in the ability to shop over narrowband users. No significant differences were observed in the Internet’s role in improving the ability to do one’s job, and in the management of personal finances.
Utilitarian (Self-Improvement) Value
Utilitarian (Functional) Value
163
3.65*
ability to do your job
0.34
ability to shop
9.29**
the way you manage your personal finances
3.50
*p < 0.05, **p < 0.01, ***p < 0.001.
Discussion At the outset, there are key differences in demographic characteristics between narrowband and broadband users. Income was a key differentiator between the two groups, with broadband users reporting higher incomes. As expected, broadband users came online much earlier compared to narrowband users, which validates their early majority status as technology users. While there were no differences between the two groups in online travel search, differences between propensities to buy travel amongst broadband users was significantly higher compared to narrowband users. Differences between the two groups across both dimensions of utilitarian value (self-improvement and functional utilitarian value) indicate a fundamental distinction in how utilitarian value is perceived as a whole with regard to the Internet. In self-improvement utilitarian value, broadband users perceive the Internet’s ability to pursue hobbies and interests to be significantly higher compared to narrowband users. This can be attributed to specific personality traits typical of broadband users. Viewed holistically, one may contend that the self-improvement dimension is more about the features of the Internet such as information richness, interactivity, and reach. Notably, the grouping of these variables together indicates a more extrinsic context. The fact that only “ability to pursue hobbies” was significant over health, people, and problems suggests that a more personality-type trait is
Section 2: Travel Website User Characteristics
evident rather than just the “relatively younger demographic in broadband users” argument. After all, early adopters are generally considered to be more innovate and are credited to have visions of their own (Parasuraman & Colby, 2001). Specific interests and hobbies can be better pursued on the Internet because of the vast information resource that it contains. Non-significant differences between other remaining parameters in this factor may be explained by the lack of their strong association with the early majority of users. It could also mean that both groups (broadband and narrowband) perceive similar levels of value in the Internet when it comes to solving problems, finding healthcare information and meeting new people. With regard to the functional utilitarian value dimension, broadband users perceive greater value compared to narrowband users as a whole. They perceive greater value in the Internet’s role with regard to their ability to shop compared to narrowband users. One plausible reason could be that their relatively greater participation in auctions and in the purchase of products, broadband users can be perceived to be more avid and accepting of emerging business models and buying propositions. In the case of handling personal finances, past research has found broadband users to have greater propensities to bank online as opposed to narrowband users (Forrester Research, 2002). However, our findings indicate that there are no significant differences in the perception of Internet’s role in improving their ability to manage personal finances. Differences across both dimensions of utilitarian value indicate that the early majority of broadband users see the Internet as a tool to improve their efficiency in general. However, there are only two univariate differences that suggest the need to delve further into the relative significance of the findings. For instance, only the “ability to shop” was found to differ significantly between the two groups in the functional dimension. That the remaining two were found non-significant highlights an underlying driver to this difference. Perceived control comes into mind because shopping is about exchange and also has physical activity associated with it in the traditional marketplace. Improving your ability in this direction
73
indicates that broadband users are more control oriented compared to narrowband users. One may argue that the “ability to manage personal finances” should be been significant too given the control argument. After all, physically going to a bank is a well recognized chore like shopping. Our contention here is that if this was revisited with a larger sample, likelihood of differences would improve on this variable, something that prior research has already indicated (Kolodinsky, Hogarth & Shue, 2000). LIMITATIONS AND IMPLICATIONS The study has its share of limitations that can be overcome in future studies. Firstly, this use of secondary data limited coverage of a few issues relevant to perceived value in the Internet. The limited number of broadband users and the resultant trimming down of the sample size to ensure parity in groups resulted in a loss of information. Future research can further validate these findings using a larger sample. The utilitarian value scale was well served, although dropping the social value scale was unavoidable. More relevant questions on the social value scale can greatly improve the reliability of the scale, and investigated in future studies. Questions pertaining to hedonic value were completely absent. Nevertheless, there is ground for strong generalizability of the findings based on the vast coverage (Continental United States) of the dataset. The study also could not control for online experience within the model, which would have provided added explanatory power to the results. It nonetheless attempted to incorporate this variable into the study, although largely different cell sizes induced a violation of the homogeneity of variance assumption in MANOVA. There are several practical and theoretical implications that arise from the findings of the study. Firstly, the study makes a fine distinction between broadband and narrowband users based on their attitudes towards the Internet. It sieves through peripheral findings, and delves deeper into user perceptions that explains much of their consumption behaviors. For travel marketers, these findings provide strong underpinnings for the development of marketing
74
HANDBOOK OF CONSUMER BEHAVIOR, TOURISM, AND THE INTERNET
strategies. For example, destination marketers can induce more broadband applications to promote key facets of their destinations. After all, destination Websites are content rich, and are aimed at improving destination image and improving the expectations of the travel experience. Findings of this study point to the fact that users who make use of functionality from broadband applications are more captive from a marketing standpoint and demonstrate greater propensities to buy travel services/products. Travel marketers can also segment the market based on the findings of the study. A range of marketing initiatives can be developed to target broadband users. To gain more precision in marketing activities, special packages or promotion offers can be targeted first at this group. Developing marketing strategies for different stages of technology adoption improves precision of marketing efforts (Parasuraman and Colby, 2001). Travel marketers can also establish key alliances with broadband service providers, which in turn can give greater access to more captive markets. Another key implication for travel marketers from these findings is the potential for Webbased advertising. Not just with the “where to place messages,” but also the types of message formats for maximum effectiveness. Web locations that vend services specific to broadband users can be suggestive venues for placing marketing messages. For example, online travel marketers can use chat rooms, online banking and stock trading sites to place marketing messages. Also, key alliances can be struck with partners engaged in online financial services to reach relatively more captive markets. The significance of functional utilitarian value and social value as predictors of online travel behavior provide insights into the online travel consumer. Given the wider acceptability of broadband, different types of message formats can be explored now. While the viability of such a move requires more detailed study, developments in Internet usage habits using multimedia applications certainly indicate potential in this direction. The key here is to acknowledge changing media consumption habits through proactive measures. After all, travel is an information intensive product well
suited for an information rich environment such as the Internet. How the industry reads signals from early adopters (broadband users) and understands their evolving needs will determine how well it exploits the capabilities of medium. Theoretically, the study paves the way for a better understanding of the online travel consumer in an evolving domain of interest. Perceived value in the Internet as a predictor of online travel behavior has not been studied earlier, and this itself is a strong theoretical contribution. As an attitudinal construct, its role as an antecedent of travel buying behavior specific to complex travel products such as vacations and cruises will be interesting. If perceived risk is accounted for to some extent, to what extent will perceived value in the Internet impact buying propensities of complex travel products? Does this have anything to do with perceived control since the findings do indicate in the direction of broadband users perceiving more control from the Internet? Perhaps it can be argued that broadband users will be more likely buyers of complex travel products, and that cruise companies and other leisure operators should target this group online more effectively. Future research should delve into potential differences in travel search behaviors between broadband and narrowband users using perceived value in the Internet as a moderator. One way of doing this is to focus on a more exhaustive scale that captures other dimensions of perceived value in the Internet, particularly the hedonic dimension. This way, marketers can extract much from the initial information search stage of the decision making process. Self-improvement utilitarian value can be specifically investigated around issues more relevant to travel. For example, particular hobbies that are more tied to or related to travel may have a significant impact on travel purchase behavior. In fact, a complete and tailored scale of perceived value constructed around strong relationships with the travel product and its features will pave the way for a finer understanding of purchasing behavior on a more widely acceptable medium.
Section 2: Travel Website User Characteristics
REFERENCES Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50: 179-211. Babin, B.J., Darden, W.R., and Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20, 644-656. Bakos, Y. (1998). The emerging role of electronic Marketplaces on the Internet. Communications of the ACM, 41 (8), 35-42. Beldona, S., Morrison, A.M., and Ismail, J., (2003). The impact of Internet usage characteristics 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. Bellman, S., Lohse, G., and Johnson, E. J. (1999). Predictors of online buying behavior. Communications of the ACM, 42 (12), 32-38. Bonn, M.A., Furr, H.L., and 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. Bourdeau, L., Chebat, J.C., and Coutourier, C. (2002). Internet consumer value of university students: E-mail vs. Web users. Journal of Retailing and Consumer Service, 9, 61-69. Bouvard, P., and Kurtzmann, W. (2002). The broadband revolution: How superfast Internet access changes media habits in American households [Online]. Arbitron/Coleman Report. Available www.arbitron. com/downloads/broadband.pdf [December 20, 2002]. Bryant, F.B., and Yarnold, P.R. (1998). Principal components analysis and exploratory and confirmatory factor analysis. In L.G. Grimm and P.R. Yarnold (eds.), Reading and Understanding Multivariate Statistics, 245-276. Washington, DC: American Psychological Association. Cho, Yong-Hyun, Wang, Y., and Fesenmaier, D. (2002). Searching for experiences: The Web-based virtual tour in tourism marketing. Journal of Travel & Tourism Marketing, 12 (4), 1-18. Chung, W., and Henderson, J.C. (2001). Americans on the Internet: Utilitarian and social participation perspectives [Online]. Sprouts: Working Papers on Information Environments, Systems and Organizations, 1. Available http://weatherhead.cwru.edu/sprouts/ papers.html [Dec 01, 2002]. Dabholkar, P.A. (1996). Consumer evaluations of new technology-based self service options: An investigation of alternative models of service quality. International Journal of Research in Marketing, 13 (1), 29-51. Darby, M.R., and Karni, E. (1973). Free competition and the optimal amount of fraud. Journal of Law and Economics, 16, 67-88.
75
Daugherty, T., Li, H., and Biocca, F. (2001). Consumer learning and 3-D ecommerce: Sequential exposure to a virtual experience relative to indirect and direct product experience, presented at 1st Experiential E-Commerce Conference, East Lansing, MI. Available at: http://mindlab.org/networkedminds/exp3d/ sessions.htm Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13 (3), 1989, 319-339. Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35 (8), 982-1003. Dwyer, R.F., Schurr, P.H., and Oh, S. (1987). Developing buyer-seller relationships. Journal of Marketing, 51 (April), 11-27. Fishbein, M., and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley. Fodness, D. and Murray, B. (1997). Tourist information search. Annals of Tourism Research, 24 (3): 503523. Forrester Report (2001). What Europe’s broadband users want [Online]. Forrester Research. Available http://www.forrester.com/ER/Press/Release/0,1769, 665,00.html [Aug 28, 2002]. Franklin, Curt (2003). Howstuffworks–How DSL works [Online]. Available http://computer.howstuffworks. com/dsl1.htm [Jul 20, 2003]. Garson, D.G. (2001). PA 765 Statnotes [Online]. An Online Textbook. Available http://www2.chass.ncsu. edu/garson/pa765/statnote.htm [May 25, 2002]. Gefen, D., and Straub, D.W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption, Journal of the Association for Information Systems, 1 (Article 8), Available http://www.jais.org [Jul 29, 2003]. Goossens, C.F. (1994). External information search: Effects of tour brochures with experiential information. Journal of Travel & Tourism Marketing, 3 (3), 89-107. Hair Jr., J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998). Multivariate Data Analysis, PrenticeHall, NJ. Hair, J.F., Anderson, R.E., Tatham, R.L., and Black, W.C. (1987). Multivariate data analysis with readings (3rd ed.), MacMillan, New York (NY). Hoch, S.J., and Deighton, J. (1989). Managing what consumers learn from experience. Journal of Marketing, 53 (April), 1-20. Hoffman, D.L., Novak, T.P., and Peralta, M. (1999). Building consumer trust online. Communications of the ACM, 42: 80-85. Holbrook, M.B., and Hirschman, E.C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9 (September), 132-140. Jackson, P., Montigni, E., and Pearce, F. (2001). Turning on broadband users. Forrester Research [Online].
76
HANDBOOK OF CONSUMER BEHAVIOR, TOURISM, AND THE INTERNET
Forrester Research. Available http://www.forrester. com/ER/Research/Report/Summary/0,1338,12349, 00.html [Nov 12, 2001]. Klein, L. (1998). Evaluating the potential of interactive media through a new lens: Search versus experience goods. Journal of Business Research, 41, 195-203. Kolko, J., McQuivey, J.L., Gordon, J., and Strohm, C.Q. (2003). Benchmark June 2003 Data Overview [Online]. Forrester Research. Available http://www.forrester. com [June 17, 2003]. Kolodinsky, J., Hogarth, J.M., and Shue, J.F. (2000). Bricks or clicks? Consumers’ adoption of electronic banking technologies [Online]. Consumer Interests Annual, 46. Available http://www.consumerinterests. org [July 15, 2002]. Kraut, R., Lundmark, V., Patterson, M., Kiesler, S., Mukopadhyay, T., and Scherlis, W. (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist, 53 (9), 1017-1031. Lee, D., Park, J., and Ahn, J. (2001). On the explanation of factors affecting e-commerce adoption. Proceedings of the 22nd International Conference on Information Systems (ICIS 2001), 109-120. New Orleans, Louisiana, December 16-19, 2001. Maignan, I., and Lukas, B. (1997). The nature and social uses of the Internet: A qualitative investigation. The Journal of Consumer Affairs, 31 (2), 346-371. Mittal, B. (1999). The advertising of services: Meeting the challenge of intangibility. Journal of Services Research, 2 (Aug.) 98-116. Morrison, A., Su, J., O’Leary, J., and Cai, L. (2001). Predicting usage of the Internet for travel bookings: An exploratory study. Information Technology and Tourism, 4 (1), 15-30. Moutinho, L. (1987). Consumer behavior in tourism. European Journal of Marketing, 21 (10), 5-44. Nelson, Philip (1970). Information and consumer behavior, Journal of Political Economy, 78 (March/ April), 311-329. NYU/PhocusWright Report (2003). Building the online vacation package marketplace: Survey results [Online]. Working Paper: Preston Robert Tisch Center for Sports and Recreation Management. School of Continuing and Professional Studies, New York University, NY. NYU. PhocusWright. Available 212.133.71.16/pdf/280103phocwright.pdf [April 15 2003]. O’Cass, A. (2001). Exploring the consumer-Internet relationship: A consumer behavior perspective. Proceedings of the Australia New Zealand Marketing Academy Conference (ANZMAC 2001). Auckland, New Zealand. December 3-5. Olson, C. L. (1976). On choosing a test statistic in multivariate analyses of variance. Psychological Bulletin, 83, 579-586. Online CISCO Documentation. (2003). CISCO Systems [Online]. Available at http://www.cisco.com/
univercd/cc/td/doc/cisintwk/ito_doc/adsl.htm [Jul 29, 2003]. Parasuraman, A., and Colby, C. (2001). Techno-ready marketing: How and why your customers adopt technology. New York: The Free Press. Paul, P. (2003). Hurry up and wait. American Demographics, 25 (5), 20-25. Pavlou, P.A. (2002). What drives electronic commerce? A theory of planned behavior perspective, Best Paper Proceedings of the Academy of Management Conference, Denver, Colorado, August 9-14, 2002. Pavlou, P.A. (2003). Consumer acceptance of electronic commerce–Integrating trust and risk with the Technology Acceptance Model. International Journal of ElectronicCommerce, 7 (3), 69-103. Pitkow, J.E., and Kehoe, C.M. (1996). Emerging trends in the WWW user population. Communications of the ACM, 39, 106-108. Rodgers, W. (1998). Analysis of cross-classified data. In L.G. Grimm and P.R. Yarnold (eds.), Reading and Understanding Multivariate Statistics, 245-276. Washington, DC: American Psychological Association. Rogers, E.M. (1995). Diffusion of Innovations. New York: The Free Press. Salisbury, W.D., Pearson, R.A., and Harrison, A.W. (2003). Who’s afraid of the World Wide Web? An initial investigation into the relative impact of two salient beliefs on Web shopping intent [Online]. Proceedings of the 1998 Association for Information Systems Americans Conference. Available http:// www.isworld.org/ais.ac.98/proceedings/track06/ salisbury.pdf [Jan 21, 2003]. Schmitt, B. (1999). Experiential marketing. Journal of Marketing Management, 15, 53-67. Sheth, J.N., and Parvatiyar, A. (1995). Relationship marketing in consumer markets: Antecedents and consequences. Journal of the Academy of Marketing Science, 23 (Fall), 255-271. Shimp, T.A., and Beardon, W.O. (1982). Warranty and other extrinsic cue effects on consumers’ risk perception. Journal of Consumer Research, 9, 38-46. Swaminathan,V., Lepkowska-White, E., and 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. Tedeshci, B. (2002). Privacy is common issue online [Online]. E-commerce Report. New York Times. Available http://www.nytimes.com/2002/06/03/technology/ 03ECOM.html [Nov 05, 2002]. Vogt, C.A., and Fesenmaier, D.R. (1998). Expanding the functional information search model. Annals of Tourism Research, 25 (3), 551-578. Weber, K. and Roehl, W.S. (1999). Profiling people searching for and purchasing travel products on the World Wide Web. Journal of Travel Research, 37, 291-298.
Section 2: Travel Website User Characteristics
Weinfurt, K.P. (1998). Multivariate analysis of variance. In L.G. Grimm and P.R. Yarnold (eds.), Reading and Understanding Multivariate Statistics, 245-276. Washington, DC: American Psychological Association. Yamauchi, Y., and Coget, J.F. (2002). Untangling the social impact of the Internet: A large scale survey. Information Systems Working Paper 1-02. The Anderson School at UCLA, California.
77
Yonish, S., Delhagen, K., and Gordon, J. (2002). Where the broadband shoppers are: Wholeview technographics research report [Online]. Forrester Research. Available http://www.forrester.com [Dec 18, 2002]. Zeithaml, V.A. (1981). How consumer evaluation processes differ between goods and services. In: Donelly, J.H. and George, E. (eds.), Marketing of Services, Illinois: American Marketing Association.
