predicting usage of the internet for travel bookings - Semantic Scholar

Information Technology & Tourism, Vol. 4 pp. 15–30 Printed in the USA. All rights reserved.

1098-3058/01 $20.00 + .00 Copyright © 2001 Cognizant Comm. Corp. www.cognizantcommunication.com

PREDICTING USAGE OF THE INTERNET FOR TRAVEL BOOKINGS: AN EXPLORATORY STUDY

ALASTAIR M. MORRISON,* SU JING,† JOSEPH T. O’LEARY,‡ and LIPING A. CAI* *Department of Hospitality and Tourism Management and †Department of Statistics, Purdue University, West Lafayette, IN ‡Department of Recreation, Park and Tourism Sciences, Texas A&M University, College Station, TX

The marketing of travel on the Internet is growing rapidly and with this so is travel e-commerce. Unfortunately, the research information to date on people searching for travel information online and booking travel through the Internet has lacked depth and sophistication. Therefore, this study developed and tested predictive models for the likelihood of booking travel online and for being a repeat booker of travel online. Using an interactive survey method, the respondents were asked to provide information on their sociodemographic characteristics, travel-related behaviors, Internet usage patterns, perceptions of the Internet, and last trips booked online. Stepwise logistic regression analysis was then applied to develop the two predictive models. A conceptual model was suggested depicting the process through which people become Internet travel bookers. Bookers Lookers World Wide Web

Repeat bookers

Internet marketing

Introduction

Online travel retailing

Internet marketing and e-commerce are irreversible trends. By using the Web and Internet as marketing tools, tourism organizations have gained some distinct advantages in cost reduction, revenue growth, marketing research and database development, and customer retention (Morrison, Taylor, Morrison, & Morrison, 1999). All the leading hotel chains, car rental and airline companies now have their own Web sites. More smaller tourism organizations are also beginning to use the Internet as a marketing tool. It will be impossible to have suc-

As the cost of computers has fallen, software improved, and the speed of telecommunications accelerated, the number of Internet users has progressively grown. There are 92 million Internet users in the US and Canada (CommerceNet, 1999). It has become extremely important for businesses to understand this new user population and take full advantage of the Internet and the World Wide Web (hereafter referred to as the Web).

Address correspondence to Prof. Alastair M. Morrison, Department of Hospitality and Tourism Management, Room 156, Stone Hall, Purdue University, West Lafayette, IN 47907. Tel: (765) 494-7905; Fax: (765) 494-0327; E-mail: [email protected]

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MORRISON ET AL.

Off

Non-Lookers Potential Lookers

Internet Travelers

L Main P

Lookers

Tick Roo R

Figure 1. Conceptual model of the development of bookers.

cessful marketing programs in the future without having an Internet strategy. The number of Internet travelers is growing rapidly. Almost all Internet users in 1998 were also travelers. Half of them got information on travel products via the Internet (“Internet travel lookers”) (Travel Industry Association, 1999) (Fig. 1). More Internet travel lookers (hereafter referred as to “lookers”) are booking online and becoming “Internet travel bookers” (hereafter referred as to “bookers”). In 1999, the number of Americans booking travel online increased by more than 80% to 11 million (PhoCusWright, 2000). Some 31% of the lookers who visited online airline sites in 1999 made reservations through the Web, compared with 21% in 1998. The percentage that booked rooms through hotel Web sites grew from 21% to 28% from 1998 to 1999, according to The NPD Group, Inc., and the percentage that booked vehicles through car rental Web sites rose from 19% to 28% in the same time span (CyberAtlas, 1999b). Furthermore, the potential for online travel is enormous. Eight million American travelers are poised to make their first online purchase (PhoCusWright, 1999). This rapidly growing population is the key target group for online travel retailers and the identification of possible customers (bookers) is crucial to their success.

Paying specific attention to the repeat booker segment has many advantages (Reid & Reid, 1993). Building repeat patronage is a means by which suppliers can increase revenues and decrease costs by reducing reliance on the much more difficult task of attracting new bookers. Repeat patrons also have the potential to be employed as a marketing resource, providing referrals and promoting positive images of online booking, which can expand the retailer’s customer base. Identifying repeat bookers could be important because appealing to the repeat bookers may be the most practical and cost-effective way of developing a business (Kang, 1998). However, until now no research has been done to determine the characteristics of repeat bookers. The primary purpose of this study was to model lookers’ probability of being bookers in terms of demographic characteristics, travel-related behavior, Internet usage patterns, Internet perception variables, and variables about the last trip booked online in the past 12 months. The second purpose was to model bookers’ probability of being repeat bookers in terms of the same five categories of variables. It was expected that the results of this study would help online travel retailers to develop more effective marketing strategies. Online travel retailers can reduce

PREDICTING INTERNET TRAVEL BOOKING USAGE marketing costs and increase their sales by efficiently identifying potential bookers and repeat bookers. Additionally, online travel retailers can increase their revenues by improving marketing strategies and providing better service to attract and retain more bookers if they know which factors significantly increase or decrease the probability of booking online. Literature Review Internet Marketing in Tourism Kasavana, Knutson, and Polonowski (1997) provided a good summary of the development of the Internet. Not until 1990 could organizations apply for Internet membership without providing valid reasons for connectivity. During the last 10 years, the Internet has revolutionized the whole business world and transformed corporate strategies, including marketing strategy (Callahan & Pasternack, 1999). The Internet represents a potentially powerful communication and distribution tool for tourism organizations. It is becoming a major force in building new relationships between customers and organizations (Quelch & Klein, 1996; Verity, 1994). Tourism organizations can eliminate the obstacles created by geography, time zones, and locations by utilizing the Internet because it enables them to communicate directly with customers (Connolly, Olsen, & Moore, 1995). Internet marketing can significantly reduce distribution and reservation through lower agent commissions and savings on reservations staff time and costs. Internet marketing mainly involves the use of the Web and e-mail. A Web site is a powerful medium offering unique marketing, advertising, product and service information, and communication opportunities between an organization and existing and potential customers (Kasavana et al., 1997; Quelch & Klein, 1996). The contents of Web pages vary according to the type and size of organization, but usually a reservations function is available at the Web sites of larger organizations. For smaller organizations, Web pages mainly serve as an information dissemination tool, but they still can provide online reservations through Internet travel services such as Travelocity.com. E-mail is a feature of the Internet mostly used for communication. Its major advantages over other communication means, such as telephone and fax,

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are its speed, access to large numbers of people at the same time, and inexpensiveness (Van Hoof & Verbeeten, 1998). E-mail provides tourism organizations with enormous marketing power and is playing a more important role in tourism. It can be used to communicate with customers directly through personalized messages realizing one-to-one relationship marketing. Internet travel retailers use e-mail to inform potential customers about limited-time offers, as well as last-minute promotions. Previous buying behaviors are recorded in databases. Confirmations can be promptly sent to online bookers by e-mail after they have made online reservations. From the travelers’ perspective, the Internet is a tool for gathering information and making reservations. Based on the WWW user survey conducted by the Graphics, Visualization, and Usability (GVU) Center in October 1997, Weber and Roehl (1999) stated that online information represented the most popular source for making travel arrangements. Moreover, the attraction of Internet marketing to businesses is the fact that people are making intensive use of the Internet and the numbers of Internet users and travel lookers are growing quickly. Travel is one of the most popular e-commerce purchase types, with 45% of online buyers saying they had purchased travel online. Only books outpaced this, at 54% (PhoCusWright, 1999). Internet Travelers’ Profiles At this time, no sophisticated analysis of lookers and bookers is available. However, this section reviews the important factors used in previous comparisons of lookers and nonlookers, lookers and bookers, and bookers and offline bookers (Fig. 1). In most previous studies, these factors were analyzed using frequencies and group differences were tested by chi-square analysis. Demographic Characteristics. Education level, age, income, and occupation have been found to be significantly different among bookers, lookers, and other Internet users (Fig. 1). Weber and Roehl (1999) found that, compared with people who searched for travel information offline, lookers were more likely to be 26 to 35 years of age and to have completed 4 years of college or to have professional or postgraduate degrees. Bonn, Furr, and Susskind (1999) found that lookers were more likely to be under 45 years

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MORRISON ET AL.

of age. Management, professional, or computer-related occupations, and households with incomes of US $50,000 or above were more likely to be lookers (Furr & Bonn, 1998; Weber & Roehl, 1999). Weber and Roehl (1999) also found that Internet travel bookers and people booking offline differed in age, education, occupation, and income. People between 26 and 55 years of age were more likely to book online. Respondents with 4-year college degrees or postgraduate degrees were more likely to purchase travel online than were people with less education. The patterns of occupation and income among those who purchased travel online and those who did not were the same as for searching for travel information online. Travel-Related Behaviors. Little research has been done on the travel-related behaviors of Internet travelers. Bonn et al. (1999) found that those using the Internet to search for travel information were more likely to stay in commercial lodging establishments, and spend more money each day while traveling. Xu (1999) tested the differences between people who purchased travel products online and those who did not using a sample of students at a major US university. She found a significant difference in travel expenditures, with bookers tending to spend more on travel than nonbookers. Kang’s study (1998) showed that there was an association between lookers and nonlookers in the self-arrangement of travel. Planning trips in advance affected Internet travel lookers’ buying behavior. Another study reported that 65% of online travelers “looked around” online before buying airline tickets (PhoCusWright, 1999). Internet Usage Patterns. Travel bookers and those booking travel offline appear to differ in years of Internet use, time spent online per week, and attitudes toward Internet use. Weber and Roehl (1999) found that Internet bookers were more likely to have used the Internet for 4 years. Other researchers have confirmed that Internet travel bookers spend more time online per week than those booking offline (CyberAtlas, 1999; Weber & Roehl, 1999; Xu, 1999). Kang (1998) arrived at the same result when comparing lookers and nonlookers. Additionally, bookers were more likely to use free sources to access Web pages, whereas those booking travel offline were more likely to state that they personally paid for Internet access (Weber & Roehl, 1999).

In analyzing the primary reasons for using the Web, Weber and Roehl (1999) stated that bookers were more likely to use the Web for “communication with others” and “wasting time,” while those booking offline were more likely to use the Web for “entertainment.” The two groups did not differ significantly in using the Web for “education,” “shopping/gathering product information,” “work,” and “gathering information for personal needs.” When comparing travel prices, travelers usually search the Web sites of online travel services instead of the pages of travel suppliers. According to The NPD Group, Inc., bookers frequented travel sites such as Travelocity.com, Expedia.com, Priceline.com, and Previewtravel.com (CyberAtlas, 1999b). Each of these services has average weekly sales of over $1 million. Internet Perception Variables. Some lookers book travel online, while others go to travel agents or call the toll-free numbers of travel suppliers after getting travel information online. The perception of the Internet as an information and reservations tool contributes to explaining this phenomenon. Kang (1998), quoting from Ostlund’s (1974) work, stated that product perception variables had great predictive power when related to purchase probability. She created a Web perception scale to predict if business travelers would adopt or reject the Web for hotel information and reservations. She demonstrated that the perceived relative advantage of the Web and the perceived room rate advantage positively affected business travelers’ Web adoption behavior. The relative advantage was defined as “the degree to which an innovation is perceived as being better than the idea it supersedes” (Rogers, 1983). Kang (1998) also found that the other perceptions of complexity, perceived risk, and ambiguity were negatively related to Web adoption. Complexity was defined as “the degree to which an innovation is perceived as being relatively difficult to understand and use” (Rogers, 1983). Perceived risk was the expected probability of economic, personal, or social problems resulting from adoption. Ambiguity represented “the degree to which a respondent felt that hotel information and reservations, and room rates on the Web are clear” (Kang, 1998). There are other online reports available to explain why lookers do not book online. Many researchers

PREDICTING INTERNET TRAVEL BOOKING USAGE agree that the main reason for this is the concern for credit card security and pricing (CyberAtlas, 1999b; Genshaft, 1999). According to the 1998 Travel eCommerce Survey by PhoCusWright, the other reasons for not booking travel online were that customers were used to contacting travel agents or corporate agencies; there were no financial incentives; customers’ knowledge about booking online was limited; and online booking was time consuming (Genshaft, 1999). The NPD Group, Inc. discovered other reasons including the need for human contact and assurances that reservations were correct (CyberAtlas, 1999b). Few studies have considered the factors that attract lookers to book online. BizRate.com found that discounts motivated lookers to make travel reservations online. Earning frequent flyer miles or points may also be a strong influence for lookers to book travel online. Frequent flyer incentives were more likely to attract online travel purchases from buyers with higher household incomes (CyberAtlas, 1999b). Thus, prices and incentives were the key to attracting lookers to book online. Among the 1998 bookers, 83% bought airline tickets, 40% reserved a hotel room, 32% rented a car, and only 3% bought a vacation or tour (Genshaft, 1999). So the type and nature of travel product seems to affect lookers in booking online. Overall, all the evidence suggests that travel ecommerce will continue to grow and that traditional travel “shopping” habits will change. In the new millennium, a fuller understanding of online travel bookers will be needed if travel e-commerce sellers are to succeed. Knowing the differences between repeat and one-time bookers, online travel retailers can more effectively identify repeat bookers and build greater loyalty among these customers. However, scientific research on bookers is still in its infancy, and no research is available that analyzes repeat bookers. Therefore, the first goal of the study was to distinguish Internet travel bookers and lookers and the second was to identify the differences between repeat bookers and one-time bookers. Methods Sampling and Data Collection The sample chosen to represent Internet travel lookers was a convenience group, graduate students

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at a major US university. The qualification for participation was based on the response of “yes” or “no” to the question: “Have you ever used the Internet for travel-related information?” A questionnaire was constructed based on previous research and online reports. The questionnaire was modified based on the results of a pilot survey to 20 graduate students at the university. Data were collected using an interactive survey method combining the Web and e-mail. The Universal Resource Locator (URL) of the questionnaire Web site was given to respondents in a personalized e-mail message. Respondents submitted their answers online and a data file was automatically generated. The interactive method had four distinct advantageous compared with a mail survey: (1) less expensive because no paper or postage was involved; (2) data collection time was shorter; (3) time was saved in data entry because the data file was automatically generated; and (4) it was easier to communicate with respondents by e-mail if they had concerns. It normally takes several weeks to collect data by mail questionnaires (Weisberg, Krosnick, & Bowen, 1996). However, in this study respondents submitted their answers in about 1 week. This method also had another advantage for this particular study, because it was more effective in getting qualified respondents. A total of 1184 e-mails were sent out to randomly selected respondents; 85 of them could not be delivered due to inaccurate or inactive e-mail addresses. Some 416 responses were received, for a response rate of 37.9%. No incentives were used in this study to achieve this response rate. Thirty-six responses (8.7%) were disqualified, as the respondents did not use the Internet for travel information. Therefore, 380 responses were used for the analysis. There were 132 respondents who just used the Internet for travel information (lookers) and 248 respondents who booked travel online (bookers). Among the bookers, 176 booked travel online more than once in last 12 months (repeat bookers). Model Specification and Variables Based on the study goals and the specific research context, a first-order multiple logistic regression model with five sets of independent variables was developed: E{Y} = [1 + exp(–β´X)]. For the first

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MORRISON ET AL.

research goal, the dependent variable (Y) was the lookers’ probability of being bookers. For the second research goal, the dependent variable (Y) was the bookers’ probability of being repeat bookers. X represented the vector of independent variables including sociodemographic characteristics, travel-related behaviors, Internet usage patterns, and Internet perception variables (Table 1). The other independent variables were about the last trip booked online. Data Analysis Descriptive statistics were used to provide background information on the respondents, including

Table 1 Summary of Independent Variables Sociodemographics Gender (male, female) Age 18–24 25–29 30+ Marital status Married Single Education Bachelor (or below) Masters Doctors degree Household income Under $19,999 $20,000–39,999 $40,000 or over Household size (continuous variable) Internet usage patterns Frequency of using the Internet for travel information Once a year or less Twice a year Once a month Twice a month or more Travel Web site visited most often Of airlines, hotels, and other travel suppliers Of online travel service (such as travelocity.com) Others Purchase of other products online (yes, no) Length of use time per week 4.9 hours or less 5–9.9 hours 10–19.9 hours 20–39.9 hours Over 40 hours Start time of using the Internet 1–3 years 4–6 years Over 7 years Personal payment for Internet service (yes, no)

sociodemographic characteristics, travel-related behaviors, Internet usage patterns, and last trip booked online. Specifically, frequencies and percentages were calculated for categorical variables, and means and standard deviations were calculated for continuous variables. Factor analysis was employed to examine the underlying patterns or relationships among 22 Internet perception statements and to group these variables into smaller sets of factors (Johnson & Wichen, 1998). Before proceeding to factor analysis, a Kaiser’s Measure of Sampling Adequacy (MSA) was used to quantify the appropriateness of factor analysis (Hair, Anderson, & Tatham, 1987). The number of factors retained was determined based on a scree plot and the Schwarz’s Bayesian Criterion (SBC). The SBC is used to determine the best number of factors and the number of factors that yields the smallest SBC value is considered best (SAS Institute, 1990). Bartlett’s chi-square significance test and Akaike’s Information Criterion (AIC) were not used to estimate the best number of factors because these two methods are inclined to include

Table 1 continued Travel-related variables Membership of frequent flyer program (yes, no) Domestic trips (continuous variable) International trips (continuous variable) Variables about the last trip booked online Expenditure made online $199.99 or less $200–399.99 $400–599.99 $600–799.99 $800 or more Airline ticket booked online (yes, no) Hotel room booked online (yes, no) Other travel products booked online (yes, no) Travel purpose Business Pleasure VFR Personal or others Trip size 1 person 2 person 3 person or more Length of planning 2 weeks or less 3 weeks 1 month 2 month 3 month or longer

PREDICTING INTERNET TRAVEL BOOKING USAGE some trivial factors (SAS Institute, 1990). The Varimax Rotation method was selected because it provides a clear separation of factors and is widely used (Hair et al., 1987). The factors obtained were used as independent variables to represent the 22 perception statements in the subsequent stepwise logistic regression analysis. Stepwise logistic regression analysis was employed to shed light on the two important research goals related to online booking behaviors: (1) Which variables significantly affected respondents’ probability of being bookers? (2) Which variables significantly affected respondents’ probability of being repeat bookers? The answers to these questions should provide valuable information for Internet travel retailers. After the logistic regression models were developed by the stepwise selection method, classification tables were created to determine how well the models predicted. Discriminant analysis was considered inappropriate for this study. If the distribution within each group (bookers and lookers) was assumed to be multivariate normal, then a parametric method could be used to develop a discriminant function. The discriminant function, also known as a classification criterion, is determined by a measure of generalized squared distance. However, in this study most of the variables were of the 0–1 variety or with restricted ranges. In these situations, multivariate normality is not a realistic assumption. Therefore, logistic regression analysis was more appropriate (Johnson & Wichern, 1998; SAS Institute, 1990). Results Bookers vs. Lookers Results of Factor Analysis. Before performing the factor analysis, a Kaiser’s Measure of Sampling Adequacy (MSA) was calculated and the overall MSA was found to be 0.87, indicating that the 22 perception statement variables met the fundamental requirements of sampling adequacy for factor analysis. Maximum likelihood factor analysis with the Varimax Rotation method was performed to group the 22 Internet perception variables. Four factors were retained based on a scree plot and the Schwarz’s Bayesian Criterion (SBC) (n = 4, SBC = –606; n = 5, SBC = –576). The Tucker and Lewis’s Reliability Coefficient for these four factors was 0.92, which

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indicated that analysis was very successful. The results of the Varimax Rotation method showed a clear pattern except for four of the perception statements (items 3, 16, 17, and 18) (Table 2). Because their loadings were very low, these four variables were eliminated from further analysis. Table 2 shows each factor with loadings, eigenvalues, and variance explained. Factor I was identified as the “disadvantages” of booking online, including the ambiguity of reservations procedures, uncertainty of reservations and cancellations, security, and difficulty of making reservations online. Factor II included the “nonfinancial benefits” of the Internet as a planning and booking tool, encompassing convenience, ease of use, and satisfaction with travel planning online. Factor III reflected the “financial benefits” of booking online and the expectation of using the Internet more in the future. Factor IV was labeled as “communicability” and included two statements, which related to the influence of friends and others. These four factors were used in the stepwise logistic regression analysis. Results of Stepwise Logistic Regression Analysis. Due to some missing values for the household income variable, a total of 343 cases were used in the stepwise logistic regression analysis. The sample size was still adequate based on the rule of at least 10 observations per parameter in the logistic model (Long, 1997). Before performing the logistic regression analysis, multicollinearity was checked by calculating the values of the Variance Inflation Factor (VIF). All the VIF values were less than 3, which indicated that there was no serious multicollinearity (Neter, Kutner, Nachtsheim, & Wasserman, 1990). Three outliers were identified that had a large influence on the regression estimate, and so they were removed from further analysis. Initially, 20 variables were used for the stepwise logistic regression analysis. Both the significance levels for entry and staying in the model were set at an alpha of 0.30. Finally, eight variables were retained in the selected model. The –2 Log Likelihood statistic was 301.646 for the selected model, and the chi-square statistic for this test was 184 (df = 14, p = 0.0001). This indicated that the overall fit of the model was very good and at least some of the explanatory variables’ coefficients were not zero at alpha = 0.05. The results of the analysis of Maxi-

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MORRISON ET AL. Table 2 Results of Factor Analysis: Bookers vs. Lookers Item

Factors

Eigenvalue

Factor I: Disadvantages 21 Unclear of online reservation procedure 19 Uncertainty of online reservation 20 Unreliability of cancellation 15 Difficulty of online booking 22 Used to travel agents or toll-free numbers 14 Unsafe of using credit card online

10.28

2.21

Factor III: Financial benefits 12 Cheap price 13 Intention of booking online more often 10 Special discounts 11 More frequent flyer miles or points

2.09

Factor IV: Communicability 1 Have heard about people booking travel online many times 2 Many friends have booked travel online

1.28

29.2%

9.5% 0.780 0.725 0.562 0.508 0.448 0.387 7.7% 0.676 0.672 0.583 0.457 5.8% 0.779 0.697

predicted probabilities for a discrete change in an independent variable is an alternative to the marginal effect that Long found more effective for interpreting a Binary Regression Model (Long, 1997). In the following interpretation, “discrete changes” were calculated for the eight significant independent variables. Although sociodemographic variables were important in distinguishing bookers and lookers in some

Table 3 Analysis of MLE of Selected Logistic Model: Bookers vs. Lookers Variable Frequency of planning travel online Most often visited Web sites of travel suppliers Most often visited Web sites of others Number of international trips Disadvantages Nonfincancial benefits Fiancial benefits Communicability Master’s degree Doctor’s degree *Significant at alpha = 0.05.

Loading

0.759 0.680 0.613 0.565 0.502 0.465

Factor II: Nonfinancial benefits 6 Easy to find the needed information 5 Convenient for checking availability and comparing prices 4 Useful for travel information 8 Convenient for booking travel 9 Time-saving of booking travel 7 Self-satisfaction of planning travel by own

mum Likelihood Estimates (MLE) for the selected model are summarized in Table 3. Using a Type I error probability of alpha = 0.05, eight variables (Master’s degree, Doctor’s degree, number of international trips, travel Web sites visited most often, other Web sites visited most often, disadvantages, financial benefits, and communicability of online booking) significantly affected lookers’ probability of being bookers. The change in the

Percentage of Variance

Wald Chi-Square

Pr > Chi-Square

Odds Ratio

2.82 4.68 4.60 4.60 44.07 1.51 44.25 5.59 6.74 6.42

0.093 0.031* 0.032* 0.032* 0.001* 0.218 0.001* 0.018* 0.009* 0.011*

1.34 0.50 0.41 1.50 0.28 1.23 3.51 1.51 2.20 4.87

PREDICTING INTERNET TRAVEL BOOKING USAGE previous studies (Bonn et al., 1999; Weber & Roehl, 1999), except for education they were not significant in this model. Compared with those with only Bachelor’s degrees, respondents with Master’s or Doctoral degrees were more likely to book online. In other words, the more education the respondents had, the more likely they were to use the Internet for booking travel. Specifically, the probability of booking online for a Master’s degree holder was 0.16 greater than for those with a Bachelor’s degree only, holding all other variables at their means. For those with Doctoral degrees, the probability of booking online was 0.27 greater than for those with a Bachelor’s degree only, holding all other variables constant. The type of travel Web sites respondents visited most often also affected the probability of booking online. Those who most often visited the Web sites of online travel services, such as Travelocity.com, were more likely to reserve travel online than those who most often visited the Web sites of travel suppliers, travel agencies, tour operators, and travel guidebooks. Specifically, for those who visited the Web sites of online travel services most often, the probability of being a booker was 0.13 higher than those who visited the Web sites of travel suppliers, and 0.17 higher than those who visited other travel Web sites, holding all other variables constant. People who had traveled to other countries in the past 12 months were more likely to be bookers than those who did not. For those who had traveled to another country once in the past 12 months, the probability of being a booker was 0.08 higher than those who had not traveled internationally. For those traveling to other countries twice in the past 12 months, the probability of being a booker was 0.07 higher than those with only one international trip, holding all other variables constant. Three of the four Internet perception factors were significant in the selected model (disadvantages, financial benefits, and communicability). If respon-

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dents were highly concerned about the disadvantages of booking online, they were less likely to be bookers. When people had positive perceptions about the financial benefits of booking online, they were more likely to be bookers. If people were more aware that other people booked online, they were more likely to be bookers. These results were similar to those found by Kang (1998), except that the “communicability” factor was significant in this study, probably because different samples were used. Kang used a sample of university faculty members, while graduate students were the sample in this study. A classification table (Table 4) was developed to evaluate how well the model predicted group membership (SAS Institute, 1990). A response was predicted to be an “event” if the estimated probability of being a booker was greater than 0.5. Otherwise, a response was predicted to be a “non-event.” The correct prediction rate was 80.6%, meaning that the logistic model correctly predicted 80.6% of the responses. The sensitivity was 86.5%, implying that 86.5% of the event responses were correctly predicted to be events. The specificity was 69.5%, meaning that 69.5% of the non-event responses were correctly predicted to be non-events. The false-positive rate was 15.9%, suggesting that 15.9% of the predicted event responses were actually non-events. The false-negative rate was 26.6%, meaning that 26.6% of the predicted non-events were events. Overall, the selected model performed well in predicting the probability of booking travel online. Repeat Bookers vs. One-Time Bookers The second study goal was to distinguish repeat bookers, one-time travel bookers, and to model bookers’ probability of being repeat bookers in terms of five sets of independent variables (sociodemographic characteristics, travel-related behaviors, Internet usage patterns, Internet percep-

Table 4 Classification Table: Bookers vs. Lookers Correct Prob. Level 0.52

Incorrect

Percentages

Event

Nonevent

Event

Nonevent

Correct

Sensitivity

Specificity

False Positive

False Negative

212

91

40

33

80.6

86.5

69.5

15.9

26.6

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MORRISON ET AL.

tion variables, and variables describing the last trip booked online). Factor Analysis. Using the sample of 248 bookers, factor analysis was again used to group the Internet perception variables. The same procedures were followed as in the comparison of bookers and lookers. First, the Kaiser’s Measure of Sampling Adequacy (MSA) was checked, and the overall MSA was 0.799, indicating that the 22 perception variables met the fundamental requirements of sampling adequacy for factor analysis. Four factors were retained based on a scree plot and the Schwarz’s Bayesian Criterion (n = 4: SBC = –590). The Tucker and Lewis’s Reliability Coefficient for these four factors was excellent at 0.90. The results were slightly different from those obtained in the analysis of bookers and lookers. Table 5 shows the results of the factor analysis for the repeat vs. one-time bookers with eigenvalues, variance explained, and loadings. These factors were used along with other independent variables to explain the probability of being repeat travel bookers. Stepwise Logistic Regression Analysis. Stepwise logistic regression analysis was performed to find a

model for predicting bookers’ probability of being repeat bookers. In total, 225 responses were used in this model selection procedure due to some missing values for household incomes. There were 157 repeat and 68 one-time bookers. Initially, 25 variables were used in the stepwise logistic regression analysis (see Table 1). Both the entry alpha and stay alpha levels were set at 0.30. It was not surprising to find that the significant variables in the final model were different from those in the logistic model for bookers and lookers. Twelve independent variables were retained in the model for distinguishing repeat and one-time bookers. Six of these variables were significant when using a Type I error probability alpha of 0.05 [travel products booked online last time, amount of use of the Internet per week, membership of frequent flyer programs (FFPs), number of domestic trips, communicability, and age]. Only one Internet perception variable, communicability of booking online (p = 0.011), significantly affected both the probabilities of being bookers and of being repeat bookers in a positive way. Table 6 presents the results of the analysis of MLE for the selected regression model.

Table 5 Results of Factor Analysis: Repeat Bookers vs. One-time Bookers Item

Factors

Eigenvalue

Factor I: Nonfinancial benefits 5 Convenient for checking availability and comparing prices 6 Easy to find the needed information 4 Useful for travel information 8 Convenient for booking travel 9 Time-saving of booking travel

7.60

Factor II: Disadvantages 21 Unclear of online reservation procedure 19 Uncertainty of online reservation 20 Unreliability of cancellation 14 Unsafe in using credit card online 22 Used to travel agents or toll-free numbers

2.44

15 Difficulty of online booking Factor III: Financial benefits 13 Intention of booking online more often 12 Cheap price 10 Special discounts 11 More frequent flyer miles or points Factor IV: Communicability 2 Many friends have booked travel online 1 Have heard about people booking travel online many times

Percentage of Variance

Loading

22.6% 0.772 0.755 0.599 0.501 0.461 9.5% 0.742 0.710 0.576 0.481 0.470 0.454

2.18

8.3% 0.617 0.612 0.532 0.501

1.59

7.0% 0.848 0.728

PREDICTING INTERNET TRAVEL BOOKING USAGE

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Table 6 Analysis of MLE of Selected Logistic Model: Repeat Bookers vs. One-Time Bookers Variable

Wald Chi-Square

Pr > Chi-Square

Odds Ratio

2.44 2.97 5.11 1.46 5.45 3.32 6.71 12.07 3.04 8.83 3.92 1.97

0.118 0.085 0.024* 0.227 0.020* 0.069 0.010* 0.001* 0.081 0.003* 0.048* 0.160

0.56 1.27 3.57 1.19 1.52 0.51 2.58 1.24 0.69 1.89 1.70 0.55

Travel Web sites most often visited Payment made online Travel products booked online Length of planning advance Length of use time per week Place using the Internet usually Membership of frequent flyer program Domestic trips Disadvantages Communicability Age Marital status *Significant at alpha = 0.05.

The type of travel products booked online (p = 0.024) was an important factor in distinguishing repeat bookers. For the last trip booked online, if respondents booked travel products other than airline tickets and hotel rooms, such as a cruise or a vacation package, they were more likely to be repeat bookers. Specifically, their probability of booking travel online more than once was 17.4% greater than other respondents, holding all other variables constant. The amount of time per week spent online (p = 0.025) was strongly related to the probability of being a repeat booker. The longer respondents spent online per week, the more likely they were to be repeat bookers. For those respondents spending 5 to 9.9 hours a week online, the probability of being repeat bookers was 8.8% higher than those who only spent 4.9 hours or less online per week, holding other variables constant. Another significant independent variable was membership in an FFP (p = 0.010), which greatly contributed to predicting the probability of being

repeat bookers and also has a very important practical implication. If respondents were members of any FFPs, their probability of being repeat bookers was 16.2% higher than nonmembers, holding all other variables at their means. The age of respondents positively affected their probability of being repeat bookers (p = 0.048). A classification table was developed to assess how well the model fit the data (Table 7). The selected logistic model predicted 73.3% of the responses correctly. The sensitivity was 82.2%, showing that 82.2% of the event (being repeat bookers) responses were correctly predicted to be events. The specificity value was 52.9%, with 52.9% of the non-event responses correctly predicted to be non-events. The false-positive rate indicated that 19.9% of the predicted events were actually non-events, while the false-negative rate showed that 43.8% of the predicted non-events were events. Overall, the selected model performed well in identifying potential repeat online travel bookers.

Table 7 Classification Table: Repeat Bookers vs. One-Time Bookers Correct Prob. Level 0.50

Incorrect

Percentages

Event

Nonevent

Event

Nonevent

129

36

32

28

Correct 73.3

Sensitivity 82.2

Specificity 52.9

False Positive 19.9

False Negative 43.8

26

MORRISON ET AL. Discussion and Implications

Implications As e-commerce continues to grow in travel retailing, it will become even more important to better understand and identify online bookers and repeat bookers. The goals of this study were to distinguish Internet travel bookers and lookers and to differentiate repeat and one-time bookers. A model with eight variables was produced through stepwise logistic model selection for predicting lookers’ probability of being bookers. The significant variables in the model were education, number of international trips, travel Web sites visited most often, Factor I (disadvantages), Factor III (financial benefits), and Factor IV (communicability). From a marketing perspective, some variables can be used to identify potential bookers, while others can help to improve Internet marketing strategy and service. This study found that the more education people have, the more likely they are to book travel online. About 65% of respondents in this study purchased travel online, compared with only 26% for the general population of lookers (PhoCusWright, 1999). In other words, once graduate students searched for travel information online, they were more likely to book through the Web. This suggests that graduate students could be a good target market for Internet travel marketing in terms of increasing the ratio of bookers to lookers. After graduation, these people are well educated and are likely to be frequent business travelers with higher income levels. They have a greater probability of being high spenders for tourism businesses. Thus, it is important for Internet marketers to further educate this group to accept the Internet as a travel reservation tool and build loyalty among them when they are still attending university. Respondents who visited the Web sites of online travel services most often were more likely to book online than the respondents who most frequently visited other travel Web. This result coincides with the research by PhoCusWright, which showed that 52% of all travel bookings in 1999 was generated through the Web sites of online travel services (PhoCusWright, 1999). People who visited the Web sites of travel suppliers were more likely to be just looking for information, and to use travel agents or call toll-free number to book travel (PhoCusWright,

1998). However, it should be noted that some Web sites of large airline companies, such as Northwest Airlines, generate substantial bookings (PhoCusWright, 1999). Some of the larger airlines are refusing to pay full rates of commission to traditional travel agencies and online travel services such as Travelocity.com (Lowengart & Reichel, 1998). Nevertheless, some smaller travel suppliers rely on online travel services to obtain online bookings. The Internet perception variables contributed greatly to predicting respondents’ probabilities of booking online. This information can help marketers to modify Internet marketing and increase online bookings. In particular, it is especially important to generate more favorable perceptions of booking travel online, as well as eliminating the unfavorable perceptions. Lookers hesitated to book travel online because of Factor I (perceived disadvantages). The same result was obtained in previous studies on bookers and lookers (CyberAtlas, 1999b; Kang, 1998; PhoCusWright, 1998). Included were perceptions of the ambiguity of reservations procedures, uncertainty of reservations and cancellations, doubts about online security, and difficulty of making reservations online. However, it can be argued that respondents’ perceptions of the lack of online security are not warranted. A way to ease lookers’ anxieties about security is for online travel retailers to develop alliances with credit card companies, so lookers will feel more comfortable with online booking when they know that a credit card company is backing it up. Quicker confirmations and responses are necessary to relieve lookers’ uncertainties about reservations and cancellations. Educating lookers to properly use the Internet for travel reservations is critical for converting them into bookers. One respondent talked about an experience where he thought he had reserved a room online, but when he arrived at the hotel the receptionist told him that they had not received his reservation. Factor III (financial benefits) was significantly and positively related to the probability of booking online, while Factor II (nonfinancial benefits) was not significant in the model. The nonfinancial benefits included convenience, ease of use, and self-satisfaction with using the Internet for travel planning and booking. This implied that respondents tended to book online to get lower prices or other financial benefits. A recent PhoCusWright survey of 500

PREDICTING INTERNET TRAVEL BOOKING USAGE online travelers found that price, above convenience and ease-of-use, was the motivating factor for purchasing airline tickets online (PhoCusWright, 2000). In this survey, the sample consisted of American online travelers who had flown on a commercial air carrier in 1999 and visited Web sites in the past month. The study suggested using lower prices or other financial benefits to attract new online bookers. It confirmed that price was the best means to entice new customers, and no other benefits (saving time, taking control, obtaining better information) could compete with the power of price. However, online travel retailers should be cautious not to ignite price wars, avoiding this by providing special offers or limited-time discounts instead of simply cutting prices. The communicability factor was significant in both logistic models, suggesting that people are more likely to book online and to frequently book travel online if they know that many other people are doing likewise. This implies that the population of bookers could grow steadily and a huge potential market for online travel retailers could emerge. This demonstrates the power of “word of mouth” in marketing and suggests that online travel retailers should take advantage of the effectiveness of word-of-mouth communications among lookers. Other studies have found that word of mouth is the most important information source in making purchase decisions (Andereck & Caldwell, 1993; Lubetkin, 1999). Referrals and informal communications from previous users are relied upon when making most travel purchase and use decisions (Reid & Reid, 1993). Some researchers have found that there is a relationship between the characteristics of travelers and reliance on word-of-mouth communications (Andereck & Caldwell, 1993; Iwamuro, 1994). Repeat bookers usually have positive feelings about online booking. Additionally, repeat bookers not only represent a stable source of revenues, they can also be information channels informally bringing friends, relatives, and other potential bookers to Internet travel retailers. Therefore, repeat bookers could be an effective conduit of word-of-mouth communications, by promoting greater awareness of online travel service and encouraging others to become bookers and then repeat bookers. Online travel retailers might encourage them to influence potential bookers to book online by using incentives. For example, re-

27

peat bookers who provide referrals to other people could be offered special discounts or bonus miles. The variables for predicting bookers and lookers were different from the ones for predicting repeat and one-time bookers. The significant variables in the repeat booking model were other travel products booked online last time, amount of use of the Internet per week, membership of FFPs, number of domestic trips, communicability, and respondent’s age. The Internet perception variables were not as important as they were for predicting bookers, with only communicability being significant. This was probably because respondents were more comfortable with Internet travel reservations after booking online once. Respondents who booked travel products, such as rental cars and vacation packages, were more likely to be repeat bookers. Normally the first travel product respondents booked online was an airline ticket or a hotel room, and most of the respondents who bought other products online were repeat bookers. Airline ticket sales represented 73% of all online travel bookings in 1999 (PhoCusWright, 1999). This may be attributed to the airline companies providing special discounts and bonus miles or flights to attract new online bookers. Once people have become comfortable with purchasing online, they may be more receptive to buying other travel products online. This implies that there is an online purchasing sequence for most lookers: airline tickets or hotel rooms first followed by other travel products. It also suggests that it is difficult to attract new bookers to purchase travel products other than airline tickets or hotel rooms. However, those who have booked airline tickets or hotel rooms online represent a lucrative potential customer group for booking other travel products online. It is believed that this is the first occasion in which FFP membership has been tested in travel e-commerce research. This variable was very important in explaining the probability of being repeat bookers, even though it was not significantly related to the probability of being bookers. If respondents were FFP members, they were not significantly more likely to be bookers than other respondents. However, once they had booked online, they were more likely to book online again. This finding makes an important contribution to the travel e-commerce literature and has a practical implication for online

28

MORRISON ET AL.

travel retailers. FFPs were initially created by airlines in the early 1980s to encourage customer loyalty from those passengers who traveled frequently. Members of these programs received free flights, gifts, or upgrades from business to first-class by converting a certain number of “air miles” accrued each time flights were taken with the same airline (Mason & Barker, 1996). Most business travelers are now members of FFPs. By offering bonus miles or points to FFP members if they book online, airline companies could attract more repeat bookers and enhance the marketing power of FFPs. FFP member databases could be a great marketing tool for other online travel retailers, because the members are potential bookers and they are easy to target. Conceptual Model Using previous research together with this study’s results, a conceptual model was developed (Fig. 1). This model should assist online travel retailers to better understand how people become online travel bookers and then to develop effective Internet marketing strategies. Internet travelers are all the people who have access to the Internet. When Internet travelers are planning trips, some use the Internet for travel information (lookers) and some do not (nonlookers). After making travel decisions, some lookers use the Internet to book travel products (bookers) and some go to travel agents or call tollfree numbers of travel suppliers (offline bookers). Most bookers are likely to reserve airline tickets or hotel rooms online for their first online travel purchase (ticket or room bookers). Once they are comfortable with online booking, they may then book other travel products online (all travel products bookers). The conceptual model also specifies current customers, main potential bookers, and potential lookers, suggesting a general marketing strategy for online travel retailers. All Internet travelers could be potential bookers, but the main potential bookers are the lookers-only. To increase the customer base, online travel retailers will benefit more from targeting this group. The lookers-only are more likely to first purchase airline tickets or hotel rooms online before any other travel products. Retailers could provide special offers on airline tickets or hotel rooms

to attract them to make the first online travel booking. If retailers want to increase the number of bookers of other travel products, such as vacation and cruise packages, the target market is the ticket or room bookers. Limitations of Study Despite the effectiveness of this analysis, the study’s findings are limited by the sample (graduate students at a major US university). Great care is needed in applying the findings of the research to other populations. The response rate (37.9%) was acceptable for this study because the sample of graduate students was quite homogeneous. For other general population samples, and in order to avoid a potential nonresponse bias, a higher response rate would be desirable. Incentives and follow-up reminders could help achieve this. Recommendations for Future Research The Internet is an innovative and fast-growing distribution tool. However, scientific research in this area is still in its infancy. Most previous research studies have focused on providing demographic profile information on Internet travelers and their Internet usage patterns. More comprehensive and creative scientific research is needed to better comprehend the travel e-commerce trend. A replication of this study is recommended using the general population of Internet travel lookers. This will provide a test of the reliability of this study’s findings. Different results for the sociodemographic variables are expected, such as occupation, age, and marital status. Further research is desirable to identify online booking behaviors and to test the proposed conceptual model of online purchasing procedures. The potential variables for this research could include first travel product booked online, date of first online travel purchase, first travel Web site used for purchasing travel products, favorite travel Web site for booking, and total number of travel products booked online. This research would help online travel retailers to better understand and retain bookers. More research is needed on repeat bookers and it is especially important to identify the reasons for repeat bookings. These future research findings will help e-commerce retailers to more effectively retain bookers.

PREDICTING INTERNET TRAVEL BOOKING USAGE Biographical Notes Dr. Alastair M. Morrison is Professor of Marketing/Tourism and Director of the Purdue Tourism & Hospitality Research Center (PT&HRC) at Purdue University, West Lafayette, IN. Before Purdue, he spent 11 years in Canada as a management consultant in the tourism and hospitality field with PKF Consulting and The Economic Planning Group of Canada. Professor Morrison has earned B.A., M.B.A., and Ph.D. degrees and is a Certified Travel Marketing Executive. He is the author of three major textbooks, including The Tourism System with Robert Christie Mill, and many articles on tourism marketing and development. Professor Morrison is a frequent presenter to industry groups on the application of Web technology in tourism and hospitality marketing.

Jing Su is a graduate student in the Statistics Department, Purdue University. She has earned her M.S. in Hospitality and Tourism Management, Purdue University, and holds a Bachelor’s degree from Peking University. Ms. Su is interested in marketing research and issues involving Internet marketing.

Dr. Joseph T. O’Leary is Professor of Outdoor Recreation and Tourism and Head of the Department of Recreation, Park and Tourism Sciences at Texas A&M University. He has published over 175 research articles and publications in various outlets including Journal of Forestry, Journal of Leisure Research, Leisure Sciences, Tourism Management, and Journal of Travel Research. Professor O’Leary has a Ph.D. from the College of Forest Resources, University of Washington, an M.S. from the School of Forestry and Environmental Studies, Yale University, and a B.S. from the School of Forestry, University of New Brunswick. He is interested in the social behavior and travel patterns of domestic and international recreation consumers, secondary analysis of major national data sets and longitudinal travel and recreation related data, the analysis of recreation and leisure trends, and the social impacts of recreation resource development.

Dr. Liping A. Cai, a former tour operator and sales manager, is an assistant professor in the Department of Hospitality and Tourism Management at Purdue University. He is the author of more than 40 refereed papers. His current research activities focus on tourist profile and behavior and destination branding. Dr. Cai serves on the editorial boards of the Journal of Hospitality & Leisure Marketing and Journal of Vacation Marketing. He earned his M.B.A. from Michigan State University, and Ph.D. from Purdue University. References Andereck, K. L., & Caldwell, L. L. (1993). The influence of tourists’ characteristics on ratings of information sources for an attraction. Journal of Travel & Tourism Market-

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ing, 2(3), 171–189. Bonn, M. A., Furr, H. L., & Susskind, A. M. (1999). Predicting a behavioral profile for pleasure travelers on the basis of Internet use segmentation. Journal of Travel Research, 37(4), 333–340. Callahan C. V., & Pasternack, B. A. (1999). Corporate strategy in the digital age. Strategy and Business [On-line]. http://www.strategy-business.com/research/99202/ page1.html Colony, G. F., Deutsch, H. W., & Rhinelander, T. B. (1995). Network strategy service: CIO meets the Internet. The Forrester Report, 12. Cambridge, MA: Forrester Consulting. CommerceNet. (1999). April 1999 Internet demographic highlights [On-line]. http://commerce.net/research/stats/ april99.html Connolly, D. J., Olsen, M. D., & Moore, R. G. (1995). The Internet as a distribution channel. Cornell Hotel and Restaurant Administration Quarterly, 39(4), 42–54. CyberAtlas. (1999a). Holidays could push online travel higher [On-line]. http://cyberatlas.internet.com/ big_picture/demographics/article/ 0,1323,6071_232421,00.html CyberAtlas. (1999b). More travelers booking online [Online]. http://cyberatlas.internet.com/big_picture/demographics/article/0,1323,6071_169421,00.html Furr, H. L., & Bonn, M. A. (1998). The Internet and hospitality marketing professional. Journal of Applied Hospitality Management, 1, 60–69. Genshaft, J. (1999). 1998 travel e-commerce survey, PhoCusWright, Inc. [On-line]. http://www.phocuswright. com Hair, J. F., Anderson, R. E., & Tatham, R. T. (1987). Multivariate data analysis with readings. New York: Macmillan. Iwamuro, R. (1994, February). Foodservice trends: “Influential” drive word of mouth. Restaurants USA,14(2), 40– 42. Johnson, R. A., & Wichern, D. W. (1998). Applied multivariate statistical analysis. Englewood Cliffs, NJ: PrenticeHall Inc. Kang, M. (1998). The perception and adoption behavior of business travelers toward the World Wide Web for hotel information. Unpublished thesis, Purdue University. Kasavana, M. L., Knutson, B. L., & Polonowski, S. J. (1997). Netlurking: The future of hospitality Internet marketing. Journal of Hospitality & Leisure Marketing, 5(1), 31– 44. Long, S. J. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks, CA: Sage Publications. Lowengart, O., & Reichel, A. (1998). Defining opportunities and threats in a changing information technology environment: The case of the travel. Journal of Hospitality & Leisure Marketing, 5(4), 57–71. Lubetkin, M. (1999). Bed and breakfasts: Advertising and promotion. Cornell Hotel and Restaurant Administrative Quarterly, 40(4), 84–90.

30

MORRISON ET AL.

Mason, G., & Barker, N. (1996). Buy now fly later: An investigation of airline frequent flyer programs. Tourism Management, 17(3), 219–223. Moore, D. S. (1995). The basic practice of statistics. New York: W. H. Freeman and Company. Morrison, A. M., Taylor, J. S., Morrison A. J., & Morrison, A. D. (1999). Marketing small hotels on the World Wide Web. Information Technology & Tourism, 2(2), 97–113. Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1990). Applied linear statistical models. Chicago: Richard D. Irwin, Inc. Ostlund, L. E. (1974). Perceived innovation attributes as predictors of innovativeness. Journal of Consumer Research, 1(1), 23–29. PhoCusWright. (1998). 1998 Travel eCommerce Survey (presentation slides) [On-line]. http://www.PhoCusWright. com PhoCusWright. (1999). PhoCusWright Travel eCommerce Survey 1999 (presentation slides) [On-line]. http:// www.PhoCusWright.com PhoCusWright. (2000). Online travel buyers choose price over convenience and ease-of-use [On-line]. http:// www.PhoCusWright.com Quelch, J. A., & Klein, L. R. (1996). The Internet and international marketing. Sloan Management Review, 38, 60–75.

Reid, L. J., & Reid S. D. (1993). Communicating tourism suppliers’ services: Building repeat visitor relationships. Journal of Travel & Tourism Marketing, 2(3), 3–19. Rogers, E. (1983). Diffusion of innovations (3rd ed.). New York: Collier-Macmillan Ltd. SAS Institute, Inc. (1990). SAS/STAT user’s guide (Version 6, 4th ed., Vol. 1 and 2). North Carolina: Author. Travel Industry Association of America. (1999). Economic review of travel in America 1998 [On-line]. http:// www.tia.org Van Hoof, H. B., & Verbeeten, M. J. (1998). Information technology Internet/network: E-mail, Web site most commonly used Internet tools. Hotel & Motel Management, 213(11), 34. Verity, J. W. (1994, November 14). The Internet: How it will change the way you do business. Business Week, 80–87. Weber, K., & Roehl, W. S. (1999). Profiling people searching for and purchasing travel products on the World Wide Web. Journal of Travel Research, 37(3), 291–298. Weisberg, H. F., Krosnick, J. A., & Bowen, B. D. (1996). An introduction to survey research, polling, and data analysis. Thousand Oaks, CA: Sage Publications. Xu, M. (1999). Profile of the World Wide Web usage pattern of college students regarding travel information. Unpublished project paper, Purdue University.