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thesis entitled

Internet Uses for Travel Information Search and Travel
Product Purchase in Pretrip Contexts

presented by
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INTERNET USES FOR TRAVEL INFORMATION SEARCH AND
TRAVEL PRODUCT PURCHASE IN PRETRIP CONTEXTS

By

800 Hyun Jun

A THESIS

Submitted to
Michigan State University
in partial fulﬁllment of the requirement
for the degree of

MASTER OF SCIENCE
Department of Park, Recreation, and Tourism Resources

2004

ABSTRACT

INTERNET USES FOR TRAVEL INFORMATION SEARCH AND TRAVEL
PRODUCT PURCHASE IN PRETRIP CONTEXT 8

By
800 Hyun Jun

lntemet has replaced or decreased the role of many traditional travel planning
tools, information or marketing communications, and purchase channels.
Despite the fast growth in Internet use for travel information search, the
population who purchases travel products online is only half of the online
information searchers. In other words, consumers use the lntemet differently as
a functional information source and shopping outlet.

This research sought to identify differences and interrelationships between
travel information search and product purchase behaviors. The problem
statement of this research was to understand online travel planning strategies
during the pretrip stage for a general population sample of Internet users, and to
examine which factors signiﬁcantly encourage or discourage online travel
information search and product purchase during pretrip planning.

This study had ﬁve distinct ﬁndings: (1) travel information search and
purchase behaviors during pretrip are different; (2) travel information search and
purchase behaviors are different by country of residence; (3) people use various
channels, such as online, ofﬂine and both on/offline, for travel information search
and purchase; (4) travel information search and purchase behaviors were more
likely to relate to a speciﬁc purpose of Internet use; and (5) patterns of travel

information search and purchase vary by travel product categories.

Dedicated to my parents, who have offered me unconditional love and support
throughout the course of this thesis.

iii

ACKNOWLEDGEMENTS

From the formative stages of this thesis, to the ﬁnal draft, I owe an immense
debt of gratitude to my advisor and mentor, Dr. Christine Vogt. Her careful
advice and guidance were invaluable as | ﬁrst studied alone in a new country.
Without her continuing encouragement and support, I would not able to continue
my study. Her passion toward an academic world and sacrifice for students,
including me, always inspire me to dream as an academic scholar and work hard
for it. I would like to thank my Committee members: Dr. Nicholls and Dr. Woods
for their knowledge, help, and support throughout this process.

I would also like to thank all of my friends for all their help. Linda Kimball
helped me to settle down in a new place and supported me as my older sister.
Ariel Rodriguez, Pam Kirbach and Chang Huh always listened to me and gave
me a good advice for my school life in MSU. Sung Jin Yi understood my hectic
life and helped me to focus on my study.

Finally, I would like to thank to my family - my parents, Yong Chan Jun and
Min Ja Ko, my sisters, Young He Jun, 800 Jung Jun and 800 Jin Jun, and my

brother, Young Woo Jun, for their continuing support throughout this process.

iv

TABLE OF CONTENTS

LIST OF TABLES
LIST OF FIGURES

CHAPTER 1
INTRODUCTION
Statement of the Problem
Purpose of the Study
Signiﬁcance of the Study
Delimitations

Limitations

Hypotheses

Deﬁnition of Terms
Organization of the Thesis

CHAPTER 2
REVIEW OF THE RELATED LITERATURE
Travel Information Search and Travel Product Purchase
The lntemet and Travel
Theory of Case-based Vacation Planning

Case-based Vacation Planning and the lntemet

Three Stages of Travel

Travel Products
Variables Inﬂuencing Online Travel Information Search and Online Travel
Product Purchase

Tourists” Demographics

Travel Experience and Latest Trip Behavior

General lntemet Use and lntemet Use for the Latest trip
Summary

CHAPTER 3

METHODOLOGY

Secondary Data Analysis

Procedures of Original Data Collection and Management
Instrumentation

Model Evaluation

Summary

vii

Ix

21
21
23
26
26
28
30

33
33

41
43

46
47
50

CHAPTER 4
RESULTS
Description of the Respondent Demographic Proﬁle
US Respondents
Canada Respondents
Results of Hypotheses Testing
Comparisons of US and Canada Samples
Comparisons of US and Canada Samples on General Background
Comparisons of US and Canada Samples on Travel Planning Behavior
during Pretrip
Comparisons of US and Canada Samples on Trip Speciﬁc Behavior
during Trip
Travel Information Search and Travel Product Purchase
Travel Information Search and Travel Product Purchase Inﬂuenced by
Tourists’ Demographics
Travel Information Search and Travel Product Purchase Inﬂuenced by
Travel Experience
Travel Information Search and Travel Product Purchase Inﬂuenced by
General lntemet Use
Travel Information Search and Travel Product Purchase Influenced by
Internet Use for the Latest Trip
Travel Information Search and Travel Product Purchase Inﬂuenced by
Latest Trip Behavior
Summary of Testing Hypotheses One to Ten
Hypotheses Acceptance
Association Between Travel Information Search and Travel Product
Purchase
Summary of Testing Hypothesis Eleven

CHAPTER 5

SUMMARY, DISCUSSION AND IMPLICATIONS
Summary

Discussion of Findings

Theoretical Implications

Managerial Implications

Future Research

Limitations of the Results

Final Comments

LITERATURE CITED

APPENDIX
Appendix 1: Selected Survey Questions and its Measurement Scales

vi

55
55
55
57
59
61
61
63

68

7O
70

78
81
90
97

106
107
115

118

119
119
122
125
126
128
130
131

133

140
141

Table 2-1:

Table 2-2:

Table 2-3:

Table 3-1:
Table 3-2:
Table 4-1:
Table 4-2:
Table 4-3:
Table 4-4:

Table 4-5:

Table 4-6:

Table 4-7:

Table 4—8:

Table 4-9:

Table 4-10:

Table 4-11:

Table 4-12:

LIST OF TABLES

Variables Signiﬁcantly Inﬂuencing Online Travel Information
Search or Travel Product Purchase

Average lntemet Usage in the US. and Canada during May 2000

The Inﬂuence of Broadband (High-speed) Internet Usage in the
US.

Population and Number of Survey Respondents

Constructs and Associated Dimensions Included in the Analysis
US Respondent Demographic Proﬁles

Canada Respondent Demographic Proﬁles

Comparison of US and Canada Samples on General Background

Comparison of US and Canada Samples on Internet Use During
Pretrip for the Latest Trip

Comparison of US and Canada Samples on Travel Information
Search Behavior During Pretrip

Comparison of US and Canada Sample on Travel Product
Purchase Behavior During Pretrip

Comparison of US and Canada Samples on Trip Speciﬁc
Behavior During Trip

Travel Information Search and Travel Product Purchase
Inﬂuenced by Gender

Travel Information Search and Travel Product Purchase
Inﬂuenced by Age

Travel Information Search and Travel Product Purchase
Inﬂuenced by Education

Travel lnforrnation Search and Travel Product Purchase
Inﬂuenced by Number of Vacation Trips in Recent 12 Months

Travel lnforrnation Search and Travel Product Purchase
Inﬂuenced by Years of lntemet Use

vii

35

39

42

46
48
56
58
62
63

65

67

69

71

73

76

79

82

Table 4-1 3:

Table 4-14:

Table 4-15:

Table 4-16:

Table 4-17:

Table 4-18:

Table 4-19:

Table 4-20:

Table 4-21:

Table 4-22:

Table 4-23:

Table 4-24:

Table 4-25:

Table 4-26:

Table 4-27:

Table 428:

Travel Information Search and Travel Product Purchase
Inﬂuenced by Time Spent Online Per Week

Travel Information Search and Travel Product Purchase
Inﬂuenced by Speed of lntemet Connection

Travel Information Search and Travel Product Purchase
Inﬂuenced by Credit Card Use for Online Purchase

Travel Information Search and Travel Product Purchase
Inﬂuenced by Planning Horizon for Internet Use for the Latest Trip

Travel Information Search and Travel Product Purchase
Inﬂuenced by Time Spent Online for the Latest Trip

Travel Information Search and Travel Product Purchase
Inﬂuenced by Length of Stay of the Latest Trip

Travel Information Search and Travel Product Purchase
Inﬂuenced by Destination of the Latest Trip

Travel Information Search and Travel Product Purchase
Inﬂuenced by Season of the Latest Trip

Summary of Hypothesis Tests One to Ten

Association of Travel Information Search and Travel Product
Purchase during Pretrip on Accommodations

Association of Travel lnforrnation Search and Travel Product
Purchase during Pretrip on Activities

Association of Travel lnforrnation Search and Travel Product
Purchase during Pretrip on Attractions

Association of Travel Information Search and Travel Product
Purchase during Pretrip on Car Rentals

Association of Travel Information Search and Travel Product
Purchase during Pretrip on Events

Association of Travel Information Search and Travel Product
Purchase during Pretrip on Flights

Summary of Associations

viii

87

89

91

95

98

101

104

111

116

116

117

117

117

118

118

Figure 1-1:

Figure 1-2:

Figure 1-3:
Figure 1-4:

Figure 4-1:

LIST OF FIGURES

Conceptual Model of Travel Planning and Travel Behaviors

Relationships between General Background, Travel Planning
Behavior During Pretrip and Trip Speciﬁc Behavior During Trip

Number of Online Travelers in the US.
Percent of Canadian Households with lntemet Use

Guide Map of Result Tables of Hypothesis Tests

ix

14
14

60

CHAPTER 1
INTRODUCTION

As the cost of computers has fallen, software improved, and the speed of
telecommunications accelerated, the number of Internet users has progressively
grown (Morrison et al., 2001 ). The Travel Industry Association of America (TIA)
estimated that 54 percent of the 209.4 million American adults (113 million
adults) used the Internet in 2002 (TIA, 2002). In 2002, an estimated 62 percent
of the nearly 12.2 million Canadian households (7.5 million households) had at
least one member who used the Internet regularly (Statistics Canada, 2003).
Online shopping also has been a growing phenomenon all over the world,
especially among countries with well-developed infrastructure for marketing
activities over the lntemet (Kau, Tang, & Ghose, 2003). According to a study
from International Data Corp, more than 600 million people worldwide will have
access to the lntemet by the end of 2002 and have spent more than US$1 trillion
buying goods and service online (Straits Time, 2002).

The Internet has replaced or decreased the role of many traditional travel
planning tools, information or marketing communications, and purchase
channels. All the leading hotel chains, car rental and airline companies currently
have their own websites (Morrison et al., 2001) and these companies encourage
customers to reserve products through these websites. All states in the US
have at least one Convention and Visitor Bureau (CVB) which hosts a website
according to the Directory of Members of the International Association of

Convention and Visitor Bureaus (IACVB, 2003). CVBs focus on lntemet

marketing more than other marketing tools since the lntemet offers distinct
advantages in cost reduction, revenue growth, marketing research, and database
development and customer retention that other marketing information systems
have not achieved (Morrison et al., 1999).

A TIA study (2001) on Americans reported that lntemet usage is higher
among travelers than the general adult population. Thirty-one percent of
American adults used the Internet for online travel planning in 2002 (TIA, 2002).
Thirty percent of Canadian households used the lntemet for travel information
and arrangements in 2002 (Statistics Canada, 2003). However, despite the fast
growth in lntemet use for information search, the population who purchases
travel products online is only half of the online information searchers. While 65
million Americans planned travel through the Internet, only 31 million purchased
travel products such as hotel rooms or airline tickets through the lntemet in 2001
(TIA, 2002). This trend suggests that many lntemet users search for information
at a variety of web-based sites, and then only buy at one site; or, that after online
information search, consumers buy ofﬂine.

Three dynamics provide possible explanations for this trend of web
information search for exceeding web purchasing. First, the gap between
information search and purchase behaviors is a reﬂection of general lntemet use.
According to lntermarket Group (1999), 64 percent of lntemet users have used
the web to do “consumer research online” but only 32 percent have made at least
one purchase online. Second, travelers’ lntemet use behaviors (i.e. searching

for information rater than purchasing online) could be an extension of traditional

travel planning and purchasing behaviors. Travel is primarily an intangible
product and travelers associate many risks with purchasing travel products
(Roehl & Fesenmaier, 1992). Therefore, travel planning can be information
intensive and result in greater amounts of information searching and processing
than the purchasing of other goods or services. Since the lntemet has intensiﬁed
the ease of collecting information, travelers may overplan or change their plans
more often (Stewart & Vogt, 1999). Third, travel information seekers may not be
acquiring information to solve a vacation problem, but instead using the lntemet
as a form of entertainment or social exchanges (Vogt & Fesenmaier, 1998).
These cases suggest that the lntemet has been used differently for information
search and purchasing.

Research on general lntemet usage, however, has neglected the study of
different usages of information search and purchase. General Internet use
studies have focused more on estimating the customer base and developing
customer proﬁles (Card, Chen, & Cole, 2003). Travel-related studies on online
behaviors have followed research approaches from general lntemet use studies;
accordingly, travel-related studies also focused mostly on studying travel
information search and purchase behaviors using consumers’ demographics as
the primary explanatory variables (Davis, Bagozzi, & Warshaw 1989; lgbaria
1994; Hwang, Gretzel, & Fesenmaier 2002). Research has shown that age,
education, gender, income and occupation inﬂuence travel-related lntemet use
(Bonn, Furr, & Susskind 1999; Card, Chen, 8 Cole 2003; Morrison et al. 2001;

So 8: Morrison 2003; Weber & Roehl 1999). However, these studies of online

travelers’ demographics have not explained the different uses of the lntemet for
travel information search and purchase behaviors.

Travel studies have expanded on those variables that may signiﬁcantly
inﬂuence online travel information search and purchase behaviors, such as travel
characteristics and online behavioral characteristics. For example, Bonn and
colleagues (1999) and Xu (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 on travel. Weber and Roehl (1999) found travelers who
purchased online were more likely to have used the lntemet for at least four
years. Few studies to date have separated online information search behaviors
from online purchase behaviors and examined how independent variables affect
online information search and online purchase. A possible reason that travel
studies have neglected understanding different uses of the lntemet for
information search and purchase is that these studies have not speciﬁcally
examined the lntemet as “a functional information source” and “shopping outlet.”
With the ever expanding lntemet, a study on lntemet usage for travel infomation

search in combination with travel product purchase is timely.

Statement of the Problem
The problem statement of this research was to understand online travel
planning strategies during the pretrip stage for a general population sample of
lntemet users, and to examine which factors signiﬁcantly encourage or

discourage online travel information search and product purchase during pretrip

planning. Speciﬁcally, the study sought to answer the following research
questions:

1. What factors signiﬁcantly affect online travel information search during
pretrip?

2. What factors signiﬁcantly affect online travel product purchase during
pretrip?

3. How do online travel information search and online travel product purchase

interrelate with each other?

The conceptual model of travel planning and travel behaviors for this research
(Figure 1-1) was modiﬁed from a conceptual model of case-based vacation
planning by Stewart and Vogt (1999). Travel planning and travel behaviors are
separated into three stages, pretrip, trip and post-trip. Travel information search
occurs during all three stages as an ongoing process. Travel product purchase
occurs from pretrip to the end of a trip, also as an ongoing process. Trip speciﬁc
behaviors occur during a trip. These variables of travel planning and travel
behaviors are shown to be interrelated, and they are affected by travelers’
general background including tourists’ demographics, travel experience and
general lntemet use. This research focused on pretrip behaviors because travel
information search and product purchase during pretrip are often an important
part of vacationing (Stewart 8 Vogt, 1999). General background and trip speciﬁc
behavior during tn'p were examined as elements which inﬂuence travel

information search during pretrip (Figure 1-2). These same elements were also

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examined for travel product purchasing. The results were compared to
understand different uses of the lntemet for travel information search and travel
product purchase during pretrip. The interrelationship between travel information
search and travel product purchase during pretrip were also examined to

understand the different roles of travel behaviors.

Purpose of the Study

The purpose of this analysis was to clarify similarities and differences in trip
planning and travel behaviors between online and ofﬂine information searchers
and purchasers. This study will help tourism marketers develop better strategies
for providing information desired by potential travelers and direct travelers to the
most efﬁcient information search and purchase channels. This is important
because many tourism-related organizations are severely reducing staff and
ofﬁces that assist consumers in planning or purchasing ofﬂine because of budget
cuts and a shift in investing in technology. This is causing ﬁrms to instead drive
consumers to use the lntemet for information searching and purchasing. In
short, this research on information search and purchase behaviors can help
government agencies who traditionally provide the most comprehensive travel
information, as well as companies in the travel industry, better understand the

growing role of the lntemet in vacation decision making.

Signiﬁcance of the Study

Travel planning research has studied decision behaviors by different stages.
Fesenmaier and Jeng (2000) showed three different decision stages that consist
of core, secondary and en route sub-decisions: core and secondary decisions
are considered in advance of the actual trip and en route sub-decisions are
considered during the trip. Stewart and Vogt (1999) showed travelers’ different
planning behaviors by three stages, during pretrip, during trip and during post-trip
(Figure 14). Research has suggested that planning during pretrip is an
important part of vacationing (Stewart 8 Vogt, 1999). Travel is inherently
intangible and experiential; therefore, consumers depend heavily on Ieaming
about products during pretrip mainly to evaluate the nature of the consumption
experience that the product can offer and how well the information and the
product obtained during the pretrip can meet the expectations of the anticipated
experience (Hoc 8 Deighton 1989; Hwang, Gretzel, 8 Fesenmaier 2002). The
lntemet can make travel easier by collecting information and purchasing travel
products prior to a trip. Currently, the lntemet is available during a trip in a very
limited way (e.g., some hotels provide access); therefore, consumers collect
travel information through the lntemet mostly before leaving. Few studies have
examined travel behaviors across three stages (during pretrip, trip and post-trip).
This research focused on travel information search and travel product purchase
behaviors during pretrip.

Published research (Bonn, Furr, 8 Susskind 1999; Card, Chen, 8 Cole 2003;

Joines, Scherer, 8 Scheufele 2003; Korgaonkar 8 Wolin 1999; Morrison et al.

2001; So 8 Morrison 2003; Weber 8 Roehl 1999) has focused primarily on
identifying and studying elements which affect travel information search or travel
product purchase with only a few studies investigating the interrelationship
between travel information search and product purchase behaviors. Stewart
and Vogt (1999) showed that people often cope with uncertainty by preparing
plans for more than one set of expected conditions, knowing that they will not
actuate all of their trips. Stewart and Vogt (1999) found travelers tend to over-
plan and consider a more diverse set of travel companions, transportation,
accommodations, and activities than they actually act on. An lntemet study
showed a gap between the number of online travel information searchers and
online travel product purchasers suggesting that over-planning may occur with
web users (eg. 64 million Americans searched for information online for travel
planning, and 42 million Americans purchased travel products online in 2003)
(Greenspan, 2004). This study will provide a clearer understanding of the
interrelationship of information search and purchasing as it compares and
examines correlations between information search and purchase behaviors.
Several studies have shown that the most popular travel product purchased
online has been airline tickets, followed by accommodations and car rentals, with
package tours purchased the least (Card, Chen, 8 Cole 2003; Mast, Shim, 8
Morgan 1991; Morrison et al. 2001). Stewart and Vogt (1999) found that
consistency of travel planning and actuating behaviors is different by travel
products (e.g., activities were shown to be the most changeable plan elements of

a vacation, as they tended to be dropped from plans with relatively few

10

additions). This suggests that traveler’s information search and purchase
behaviors are different by travel products. In this study, travel information search
and travel product purchase behaviors are detailed by six travel products,

accommodations, activities, attractions, car rentals, events and ﬂights.

Delimitations

The study was delimited to the following:

1. A secondary dataset funded by Canadian Tourism Commission (CTC).
This dataset was analyzed with the assistance of Scott Meis and SECOR, a
consulting ﬁrm.

2. Those US and Canada residents who have web access and were willing to
complete the online survey instrument. This involved, ﬁrst, using the lntemet for
travel or vacations (i.e., planning, researching, reserving, or paying credit card for
travel product), and second, taking a vacation in 12 months between November
2000 and October 2001. The sample was further qualiﬁed by those residents
who completed the survey instrument. I

3. Travel information search and product purchase behaviors were
delimitated to the latest trip in the past 12 months. The behaviors studied
occurred during the pretrip stage of a latest trip per the respondent, but the
survey was conducted after this latest trip.

4. Only three general background topics were selected from the literature to
examine their inﬂuences on travel information search and product purchase: (1)

tourists’ demographics, (2) travel experience, and (3) General lntemet use

11

(Figure 1-2). There are many more background variables to study such as
values, geography, or interests.

5. Four channels to search for travel information and purchase travel products
were: (1) online, (2) ofﬂine, (3) both online and ofﬂine (on/ofﬂine), and (4) not
applicable (none) (Figure 1-2).

6. Six travel products for the latest trip were selected to examine different
travel information search and product purchase behaviors: (1) accommodations,
(2) activities, (3) attractions, (4) car rentals, (5) events, and (6) ﬂights (Figure 1-
2). Other products might have been included, such as cruises and tours, clothes
and other articles for the trip, or restaurants.

7. Statistical inferences were delimited to descriptive statistics (cross-
tabulation), comparisons of differences in travel information search and travel
product purchase (Chi-square tests) and measures of association between travel

information search and travel product purchase (Contingency Coefﬁcient tests).

Limitations

The study was limited by the following factors:

1. The sample of residents was limited to those who have lntemet access,
used the lntemet for travel or vacation purposes, and traveled or had vacation
experience(s). Therefore, this sample may not be representative of all US and
Canada residents. This was minimized by matching the respondent dataset to

national demographic proﬁles of Americans and Canadians. Canadian data were

12

weighted by the primary data collectors on education and marital status to better
represent the national demographic proﬁle.

2. Travel information search and travel product purchase behaviors happened
during pretrip contexts, but the survey was conducted after the trip. In addition,
those information search and purchase behaviors were deﬁned for the latest trip
in the past 12 months. Accordingly, respondents may have used a variety of
benchmarks to recall these experiences and may have misrepresented behaviors
(Vogt, 1993).

3. Data were collected in 2001; therefore, the study may not represent
Internet use levels in 2004. However, according to a trend study by TIA (2004),
the number of American online travelers from 2001 to 2003 is quite similar
(Figure 1-3). The growth of the lntemet use in Canada has slowed down from
2001 to 2002 (Statistics Canada, 2003) (Figure 1-4). Therefore, it may be
assumed that the time gap in data collection between year 2001 and 2004 may
not be critically different.

4. As this study uses secondary data, the original physical survey
questionnaire and coding book were provided in a report format. Since the
secondary data analysts were not involved in collecting the data, the data
analysis may be less than perfect in fully understanding the meaning and
accuracy of the data and the meaning (or deﬁnition) of words used for data
collection (Trochim, 2001). To minimize the potential error or bias from original
data analysis, the primary researcher, Mr. Scott Meis, was consulted in this

research analysis.

13

Figure 1-3
Number of Online Travelers in the US.

Millions of US. online travelers

 

120

100 .. —G O 96

90 95 96

 

80 -
77

so . 65

40 - 42

20 . 27

 

 

0 j T I I I I I

1996 1997 1998 1999 2000 2001 2002 2003
Source: Travel Industry Association of America (2004)

Figure 1-4
Percent of Canadian Households with Internet Use

Percent of Canadian households with at least one regular Internet user

 

 

1 00%

80% -

40 62%

 

60% -

40% -

 

36%

20% *

 

 

 

0% I I I I
1998 1999 2000 2001 2002

Source: Statistics Canada (2003)

14

Hypotheses
The study was designed to test the following hypotheses (Figure 1-2):
Past research suggests that travel information search behaviors in a pretrip
context differ by tourists’ demographics, travel experience and general lntemet

use. This study will test for these types of signiﬁcant predictions.

1. Tourists’ demographics signiﬁcantly differ in regard to travel information
search behaviors during pretrip.
1a. Country of residence differs on information search behaviors.
1b. Gender differs on information search behaviors.
10. Age differs on information search behaviors.

1d. Education differs on information search behaviors.

2. Travel experience signiﬁcantly differs in regard to travel information search
behaviors during pretrip.
2a. Number of vacation trips in recent 12 months differs on information

search behaviors.

3. General lntemet use behaviors signiﬁcantly differ in regard to travel
information search behaviors during pretrip.
3a. Years of lntemet use differs on information search behaviors.
3b. Time spent online per week differs on information search behaviors.

3c. Speed of lntemet connection differs on information search behaviors.

15

3d. Credit card use for online purchase differs on information search

behaviors.

Past research suggests that travel product purchase behaviors in a pretrip
context differ by tourists’ demographics, travel experience and general lntemet
use. This study will test for these types of signiﬁcant predictions.

4. Tourists’ demographics signiﬁcantly differ in regard to travel product
purchase behaviors during pretrip.

4a. Country of residence differs on product purchase behaviors.
4b. Gender differs on product purchase behaviors.
4c. Age differs on product purchase behaviors.

4d. Education differs on product purchase behaviors.

5. Travel experience signiﬁcantly differs in regard to travel product purchase
behaviors during pretrip.
5a. Number of vacation trips in recent 12 months differs on product

purchase behaviors.

6. General lntemet use behaviors signiﬁcantly differ in regard to travel product
purchase behaviors during pretrip.
6a. Years of lntemet use differs on product purchase behaviors.
6b. Time spent online per week differs on product purchase behaviors.

6c. Speed of lntemet connection differs on product purchase behaviors.

16

6d. Credit card use for online purchase differs on product purchase

behaviors.

Travel information search and/or travel product purchase behaviors in a
pretrip context may differ by lntemet use for the latest trip during pretrip. This
study will also test for these types of signiﬁcant predictions.

7. lntemet use for the latest trip planning signiﬁcantly differs in regard to travel
information search behaviors during pretrip.

7a. Planning horizon for lntemet use for the latest trip differs on information
search behaviors.
7b. Time spent online for the latest trip differs on Information search

behaviors.

8. lntemet use for the latest trip planning signiﬁcantly differs in regard to travel
product purchase behaviors during pretrip.
8a. Planning horizon for lntemet use for the latest trip differs on product
purchase behaviors.
8b. Time spent online for the latest trip differs on product purchase

behaviors.

Past research suggests that trip speciﬁc behaviors during trip is inﬂuenced or

strongly related to pretrip travel information search and/or travel product

17

purchase behaviors (Stewart 8 Vogt, 1999). This study will test for these types
of signiﬁcant predictions.
9. Latest trip behavior signiﬁcantly differs in regard to travel information
search behaviors during pretrip.
9a. Length of stay of the latest trip differs on information search behaviors.
9b. Destination of the latest trip differs on information search behaviors.

9c. Season of the latest trip differs on information search behaviors.

10. Latest trip behavior signiﬁcantly differs in regard to travel product
purchase behaviors during pretrip.

10a. Length of stay of the latest trip differs on product purchase behaviors.

10b. Destination of the latest trip differs on product purchase behaviors.

10c. Season of the latest trip differs on product purchase behaviors.

The ﬁnal hypothesis tests the interrelationship between travel information
search and travel product purchase behaviors across travel products.
11. Travel information search behaviors and travel product purchase

behaviors during pretrip are associated, however, not perfectly.

18

Deﬁnition of Terms

The following terms are deﬁned to clarify their use in the study:

Travel Information search: “...the motivated activation of knowledge stored
in memory or acquisition of information from the environment...a search can be
either lntemal or external. lntemal search is based on the retrieval of knowledge
from memory, while the other consists of collection of information from the
marketplace” (Engel et al., 1995).

Travel product purchase: Dictionary.com deﬁnes “purchase” as “to obtain
in exchange for money or its equivalent; and to acquire by effort.” Since travel
products are intangible and experiential, purchasing travel products does not
mean to own goods as a result of exchanging with money. In tourism, making a
reservation for travel products is considered as a purchase.

Travel planning: “...the use of information, the formation and change of
preferences, and the process of judging of alternatives...” (Stewart 8 Vogt, 1999,
p. 80). ”...an individual’s reasoned attempt to recognize and deﬁne goals,
consider alternative actions that might achieve the goals, judge which actions are
most likely to succeed, and act on the basis of those decisions” (Hoc, 1988).
Travel planning during pretrip includes travel information search and travel
product purchase.

Online: The Ofﬁce of Information Technology of Ohio State deﬁnes “online”
as “a term that has commonly come to mean "connected to the Internet." It also
is used to refer to materials stored on a computer (e.g., an online newsletter) or

to a device like a printer that is ready to accept commands from a computer.”

19

Organization of the Thesis

Beyond the introduction chapter of this thesis, four additional chapters will
examine travel information search and product purchase behaviors during pretrip
across six travel products. Chapter 2 contains the literature review. The
literature review describes travel characteristics, travel information search
behaviors and travel product purchase behaviors, speciﬁcally, in the lntemet
environment. The theory of case-based vacation planning will be examined to
understand how case-based vacation planning can be applied to online travel
behaviors. Variables inﬂuencing travel information search and product purchase
will be examined. A methodology section, Chapter 3, will discuss advantages
and disadvantages of the use of secondary data. It will also describe the
procedures of original data collection and management. It will then explain how
the original data were modiﬁed for this analysis and test hypotheses. Chapter 4
will describe the results. Finally, in Chapter 5, the ﬁndings and implications of the
study will be discussed, and recommendations will be made for future research in

this area.

20

CHAPTER 2
REVIEW OF THE RELATED LITERATURE
The literature related to travel information search, decision making and
product purchase on the Internet has evolved over the past ﬁve years. Past
research has focused on developing elements that inﬂuence online information
search and purchase. Few studies have speciﬁcally examined the growing
lntemet use as a functional information source and a shopping tool. In this
chapter, literature related to travel information search and product purchase
behaviors and elements that affect online travel information search and product
purchase behaviors was reviewed. The review of literature is organized into the
following topics: (1) travel information search and travel product purchase, (2) the
Internet and travel, (3) the theory of case-based vacation planning, (4) variables
inﬂuencing online travel information search and product purchase, and (5) a

summary.

Travel Information Search and Product Purchase
When travelers realize they need to make a decision (i.e., purchase travel
products), internal information search begins, such as considering previous
experiences and knowledge (Chen 8 Gursoy 2000; Fodness 8 Murray 1997;
Vogt 8 Fesenmaier 1998), as well as active external information search, such as
looking for travel guide books, brochures, websites and so on (Beatty 8 Smith
1987; Gursoy 8 McCIeary 2004). Understanding when and how travelers search

for information is important for marketing management decisions, design of

21

effective communication campaigns, and service delivery (Srinivasan 1990;
Wilkie 8 Dickson 1985). During travelers’ information acquisition, marketers can
inﬂuence travelers’ buying decisions (Schmidt 8 Spreng, 1996). Therefore,
information search has been one of the most examined subjects in consumer
tourism studies (Schmidt 8 Spreng, 1996).

Travel information search and product purchase behaviors have been
examined differently from other goods’ information search and purchase
behaviors because of travel characteristics. Travel planning can be information
intensive, and result in greater amounts of information search and processing,
than the purchasing of other goods or services as travel is inherently experiential
(Hwang, Gretzel, 8 Fesenmaier, 2002). While consumer goods typically
comprise of a well-deﬁned range of tangible/searchable attributes and, to a
lesser extent, of intangible/experiential aspects, travel is a complex bundle of
experiences with only a small set of tangible components (Hwang, Gretzel, 8
Fesenmaier, 2002). Travel products cannot be experienced even when an
individual purchases the products, until they actually take the trip. Hence,
travelers often feel anxiety and uncertainty about the travel product/service
outcomes and feel they have no or little control on purchasing travel
products/services (Weiermair, 2000). To reduce uncertainties, many travelers
value information acquired during vacation planning (McCIeary 8 Whiteny, 1994).

Purchasing travel products involves a relatively high level of perceived risk
since possibilities for prepurchase evaluation are limited because of travel

product characteristics such as intangibility and perishability (Nysveen, 2003).

22

 

For example, when a traveler purchases a travel product (travel experience), the
person may receive a picture or brochure and a few pieces of paper with

conﬁrmation numbers, but the actual experience will not happen until his/her trip
begins. In addition, travel products cannot be owned; therefore, there is nothing

left except memories after using (experiencing) the product.

The Internet and Travel

The lntemet represents a recent technological innovation that has had a
profound impact on all facets of people’s lives (Weber 8 Roehl, 1999). A study
conducted by CyberAtlas reported that Personal Computer (PC) use in American
homes had surpassed the 50 percent mark, and that 90 percent of PC users
were online, in 2000 (Pastore, 2000a). TIA (2004) reported that more than 64
million Americans (67 percent of American lntemet users who travel) used the
lntemet in 2003 to acquire information on destinations or to check prices or
schedules. Thus, nearly two-thirds of the 95.8 million American lntemet users
who travel consulted online resources for trip planning. As the lntemet is
becoming a mainstream technology, the lntemet is considered as an important
medium for business (Joines, Scherer, 8 Scheufele, 2003). Tourism is among
the most prospering industries in the electronic marketplace (Nault 8 Dexter,
1995). From the perspective of travel product suppliers, the lntemet can
eliminate the obstacles created by geography, time zones, and locations
because it enables the suppliers to communicate directly with customers

(Connolly, Olsen, 8 Moore 1995; Morrison et al. 2001). In addition, lntemet

23

marketing can signiﬁcantly reduce distribution and reservation costs through
lower agent commissions and savings on reservation staff time and costs
(Morrison et al., 2001).

Consumers ﬁnd the lntemet is convenient, and it is easy to ﬁnd competitive
pricing. According to a consumer shopping study, about 67 percent of the online
shoppers agreed that convenience was the main reason they bought via the
lntemet, and 41 percent mentioned price as the second most important reason
(Chiger, 2001). The lntemet has made it easier for travelers to store, collect and
exchange information. As travel planning is information intensive and results in
greater amounts of information search and processing, more and more travelers
are utilizing the lntemet and online resources for their information needs (Gursoy
8 McCleary, 2004). Moreover, it is likely that because of low cost and ease of
retrieving the information from online sources, more and more consumers will
expand information search efforts through the lntemet than to try to remember
past experiences even for people who are highly familiar with the destination
(Gursoy 8 McCleary 2004; Stigler 1961 ).

Some consumers may not purchase online due to the risks associated with
lntemet shopping, such as a possibility of credit card fraud, the inability to touch
or feel something before buying it, and the problems with returning products that
fail to meet expectations (Bhatnagar, Misra, 8 Rao 2000; Wee 8 Ramachandra
2000). Despite the fast growth of lntemet use in travel information search,
information searchers do not always purchase travel products online. According

to a TIA study, while 65 million Americans planned travel through the lntemet,

24

only 31 million Americans purchased travel products from the lntemet in
2001(Greenspan, 2004). Morrison and colleagues (2001) found reasons for not
buying on the lntemet included perceptions of the ambiguity of reservation
procedures, uncertainty of reservations and cancellations, doubts about online
security, and difﬁculty of making reservations online. However, once people
become comfortable with purchasing online, they are more receptive to buying
other travel products online (Morrison et al., 2001). Moreover, various and
almost limitless information through texts, images and Video on Demand (VOD)
on the lntemet can reduce the uncertainty about travel products and their
purchases.

In summary, the lntemet has intensiﬁed travel information search behaviors
because of the conveniences offered. However, there are barriers to lntemet
purchasing such as consumers’ perceived disadvantages in the purchasing
process. Therefore, a gap between online travel information search and online
travel product purchase is expected. Three possible explanations were provided.
First, many lntemet users search for more travel information than they need for
purchasing a travel product as an extension of travel planning and purchasing
behaviors. Stewart and Vogt (1999) found that, during pretrip, travelers tended
to overplan and actuated fewer elements than they planned. Second, after
online travel information search, consumers buy travel products ofﬂine as a
reﬂection of general lntemet use. lntermarket Group (1999) found that 64
percent of lntemet users have used websites for information search, but only 32

percent have actually made at least one purchase through the lntemet. Third,

25

people buy during trip (e.g., en-route or upon arrival). Through these three
explanations, it can be assumed that individuals used the lntemet differently for

travel information search and travel product purchase.

Theory of Case-based Vacation Planning

The theory of case-based vacation planning was developed by Stewart and
Vogt (1999) from the theory of case-based planning by Hammond (1989).
Stewart and Vogt (1999) studied the extent to which the case-based planning
model captures the mechanisms of travel planning and, if so, what it reveals or
explains about travel planning behavior. The lntemet has changed travel
planning and decision behaviors. This research studied how the lntemet
changes travel information search and travel product purchase behaviors during
pretrip. The theory of case-based vacation planning is applied to examine
differences of travel information search and travel product purchase in the
lntemet environment (Figure 1-1).

This section includes: (1) case-based vacation planning and the lntemet, (2)

three stages of travel, and (3) travel products.

Case-based Vacation Planning and the Internet
The theory of case-based planning is the idea of planning as remembering
and Ieaming (Hammond, 1989). Individuals practice case-based planning as
storing cases in memory, which then serve as the initial knowledge base for

tackling a new planning situation (Stewart 8 Vogt, 1999). In the planning

26

F...

process, the constant problems arising from goal interaction, sequencing, and
environmental change, lead to the development of the theory of case-based
planning (Hammond, 1989). Case-based planners predict problems based on
their own experiences so that they can match plans that avoid previous problems
(Hammond, 1989). Thus, individuals learn speciﬁc repairs or solutions to
planning problems that can be applied again when similar problems arise
(Hammond, 1989).

Case-based planners retain many of the assumptions underlying earlier
models, that is, that the planner has a store of knowledge together with a means
of accessing it and uses rules for applying knowledge (Stewart 8 Vogt, 1999).
But case-based planning speciﬁes a different and more efﬁcient means of plan
production than the create-and-debug method (Stewart 8 Vogt, 1999). Rather
than constructing a new plan from basic rules each time one is needed, case-
based planners retrieve and then elaborate on previous plans (Hammond, 1989).
The lntemet has enhanced sharing individuals’ travel experiences. Hence,
individuals are able to store various cases for predicting problems that possibly
occur during their trip, and they prepare solutions through the lntemet. Before
the lntemet appeared, individuals learned primarily from their own past travel
experiences or stories from their friends or relatives, or those in their “physical”
social network.

One characteristic of plan formation derived from case-based planning is the
expectation that people anticipate and prepare for contingencies, rather than

assuming that everything will go as planned (Berger 8 Dibattista 1992; Stewart 8

27

Vogt 1999). Traditional planning acknowledges that people often cope with
uncertainty by preparing plans for more than one set of expected conditions,
knowing that they will not actuate all of their plans (Stewart 8 Vogt, 1999).
Stewart and Vogt (1999) found three unique characteristics of planning from
case-based planning: the plans developed by all planners will be subject to
change as they are actuated; people plan more than they will actuate, as they
compensate for congestion and uncertainty by overplanning; and experience
teaches people how to plan.

These three scenarios that Stewart and Vogt (1999) reported using case-
based vacation planning supports online travel planning behaviors. The lntemet
has made it easier for individuals to collect travel information before leaving
home. Hence, travelers can easily overplan and choose one option in their ﬁnal
decision. As huge amounts of information have become available through the
lntemet, information searchers are required to develop their own planning
strategies to be able to access and use their information to solve their problem
and reach their goal. The more that people use the lntemet for travel and other

purchases, the more they will Ieam how to plan travel effectively.

Three Stages of Travel
Steward and Vogt (1999) found case-based planning speciﬁes a sequence of
activities that the planner undertakes. Stewart and Vogt (1999) developed a
conceptual model of case-based vacation planning with three sequential stages

in relation to vacation planning, from pretrip through post-trip (Figure 1-1). Travel

28

is comprised of opportunities to make many choices. Travelers can choose a
destination, travel companions, lodging, restaurants, activities, modes of
transportation, and so on (Stewart 8 Vogt, 1999). Because of complex decisions
of these choices, the travel decision process can be described as having a “net
structure” (Fesenmaier 8 Jeng, 2000). A net structure means that every aspect
of each decision affects and is being affected by other decisions (Fesenmaier 8
Jeng 2000; Mountinho 1987). Fesenmaier and Jeng (2000) showed three
different, but sequential, decision stages, that are similar to the three sequential
stages from case-based vacation planning. The decision stages by Fesenmaier
and Jeng (2000) consist of core, secondary and en route sub-decisions: core
decisions are usually planned ahead of time, in detail, and include sub-decisions
of primary destination, date/length of trip, travel party, accommodation, route,
and budget; and secondary sub-decisions are tentative decisions and include
secondary destination(s) choice, selections of activities, and choosing attractions
to visit. Even though secondary sub-decisions may be considered in advance of
the actual trip, they are moderately ﬂexible in order to accommodate possible
changes in an itinerary (Fesenmaier 8 Jeng, 2000). En route sub-decisions are
choices that are considered during the trip. Woodside and King (2001) separated
vacations into three stages including “before trip,” “during trip” and “after trip.”
These researchers have coined this sequencing as a purchase consumption
system, which is a sequence of purchases the consumer undertakes where the
purchase of one travel product may lead to the purchase of others. In

conclusion, travel behaviors are separated into three stages, and they are

29

sequent and interrelated. Travel information search, decision making and
product purchase behaviors may need to be studied separately in three stages of
travel (i.e., during the pretrip, trip and post-trip).

Information search and purchase during pretrip are often an important part of
vacationing (Stewart 8 Vogt, 1999). Travelers’ aversion to risk, desire to save
money, or the need to satisfy the preferences of a diverse group of travel
companions leads them to engage in deliberate considerations and detailed
advance arrangements, thus, planning takes on great importance (Fesenmaier 8
Lieber 1988; Roehl 8 Fesenmaier 1992; Stewart 8 Vogt 1999). The lntemet has
intensiﬁed pretrip information searching and purchasing. Various and almost
limitless information through texts, images and VOD on the lntemet has
encouraged travelers to plan and purchase prior to a trip. For example, currently,
people can check images of a hotel room and read other customers’ experience
in the hotel through the lntemet. This information makes people comfortable to

purchase (make a reservation) the hotel room prior to a trip.

Travel Products
According to the case-based vacation planning, trip plans are subject to
change when they are actuated, and people plan more than they will actuate
(Stewart 8 Vogt, 1999). Stewart and Vogt (1999) found that the amount of
information searched for and purchasing patterns differed by travel products.
Travelers tended to overplan and consider a more diverse set of travel

companions, transportation, accommodations, and activities than they actually

30

act on (Stewart 8 Vogt, 1999). Speciﬁcally, activities were shown to be the most
changeable plan elements of a vacation, as they tended to be dropped from
plans with relatively few additions (Stewart 8 Vogt, 1999). Other research
supports this ﬁnding. Fodness and Murray (1999) suggest features of each
product clearly affect information search. Woodside and MacDonald (1993)
presented a general system framework for understanding different tourists’
choice by travel products, such as choice of destinations, accommodations,
transportation, subdestinations, attractions, and activities. Woodside and
McDonald (1993) concluded that these travel product decisions could be different
but interdependent with each other. As shown through research studies,
travelers are likely to have different planning and purchasing strategies by travel
products.

Researchers have studied which travel products were purchased online, and
found online travelers tended to purchase airline tickets ﬁrst, followed by
accommodation and car rentals, with package tours being purchased the least
(Card, Chen, 8 Cole 2003; Morrison et al. 2001). Card, Chen, and Cole (2003)
explained that airline ticket services are transactional services involving a
purchase rather than an exchange of information, whereas package tours involve
more information seeking and are generally more expensive than airline tickets.
Morrison and colleagues (2001) categorized ﬂights, lodging and car rentals as
low-risk travel products for online purchasing, and travel packages as a high-risk
product. Consumers feel more comfortable purchasing low-risk travel products

online and online sites concentrate on these items (Card, Chen, 8 Cole, 2003).

31

For example, travel-related web sties such as Priceline.com and Travelocity
concentrate on selling airline tickets, lodgings, and car rentals and turned a proﬁt
mostly from these products and not high-risk travel packages (Forrester
Research, 2001). While examining popular travel products for online purchasing,
researchers uncovered a sequential pattern. Morrison and colleagues (2001)
found there was an online purchasing sequence for most information searchers:
airline tickets or hotel rooms ﬁrst, followed by other travel products. Those who
have purchased airline tickets or hotel rooms online represent a lucrative
potential customer group for purchasing other travel products online (Morrison et
al., 2001). The literature suggests future study topics including which travel
products are searched or purchased online during the three different stages
(pretrip, during trip and after trip); how online information search and purchase
behaviors are different by travel products; and which elements affect online
information search and purchase behaviors across different travel products.

This section explained case-based vacation planning. In addition, it
discussed how the Internet has enhanced case-based vacation planning.
Stewart and Vogt (1999) found other characteristics of travel behavior through
case-based vacation planning: travel behaviors occur in three sequential stages;

and travel behaviors differed by travel products.

32

Variables Inﬂuencing Online Travel lnformatlon Search and Online Travel
Product Purchase

Researchers, to date, have focused on the study of elements that signiﬁcantly
inﬂuence online travel information search or/and purchase. Consumers’
demographic proﬁles have been considered as the primary explanatory variables
(Davis, Bagozzi, 8 Warshar 1989; Hwang, Gretzel, 8 Fesenmaier 2002; lgbaria
1994). Travel characteristics and general lntemet use behaviors have also been
studied as explanatory variables. This research reviews these variables and
their inﬂuence on online travel information search and travel product purchase.
Other variables, that have been studied on travel planning and decision making
behaviors before the lntemet, are also reviewed and added as explanatory
variables.

This section includes: (1) tourists’ demographics, (2) travel experience and
latest trip behavior, and (3) general lntemet use and lntemet use for the latest

trip.

Tourists' Demographics
In tourism research, demographics have been considered as important
variables for explaining travel behaviors such as destination choice and
information search (Hwang 8 Fesenmaier, 2004). Research has shown that age,
household income, occupation and education signiﬁcantly inﬂuence travel-related
lntemet use (Bonn, Fun, 8 Susskind 1999; Card, Chen, 8 Cole 2003; Morrison et

al. 2001; So & Morrison 2003; Weber & Roehl 1999) (Table 2-1). Bonn, Furr,

33

and Susskind (1999) concluded that lntemet users (vs. non lntemet users) who
searched for travel-related information were more likely to be younger than 45
years of age and college-educated owners of computers. Weber and Roehl
(1999) segmented online information search and online purchase behaviors in
their study. Respondents who searched for information online (vs. ofﬂine) were
likely to be 26-35 years old. Online purchasers were likely to be 26-35 years old,
as well as slightly older (36-55 years old). In both information search and
purchase, online users were likely to be more educated (4-year college degrees
or more), had higher income ($50,000 or more), and held management!
professional/ computer related occupations.

When the lntemet was introduced, lntemet users were dominated by males,
whites, more highly educated and higher income individuals, and those who held
professional] computer-related jobs (Table 2-1). Demographic proﬁling of
lntemet users and nonusers was very important to understand the use of this
new innovation. Since its introduction, there have been changes in the
demographics of lntemet users. Early studies found gender and race were
important inﬂuencing elements in lntemet use (Mirror 1995; Pitkow 8 Kehoe
1996; Yankelovich Partners 1995). A few years later, Korgaonkar and Wolin
(1999) found gender not to be signiﬁcantly correlated with online information
search, but still signiﬁcantly related to online purchase. Other recent research
also suggests that gender and race did not signiﬁcantly inﬂuence between online
travel information search and nonsearch and/or between online travel purchase

and nonpurchase (Bonn, Furr, 8 Susskind 1999; Morrison et al. 2001;

34

 

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00000.50 00:00.0. .0>0._.... .0 000000 00:00:80. .020... 00:00 3000000. 200000.090 0030...;

TN 030...

35

 

000:000 .00. 000000.000 00 0000000000: 000 00000000 0002000 00:00. 00.000000 000 0003 0005 0000:. $2 .0
0.0.00 05 0. 0:00: 0: 003 0000. .50 0.02:0 05 00 000.00 0003 00.00.09 0:005 $2 .0
00.030 .00 0003 00.00005 0000:. 0.2 .0

 

«0.00000

0.00

00.00:. 06:00:00 0090 0:00:00: 00000000.

00 80.00» 000 000000 002.00. .0> 880.

00:00:00 .0 0.0>0. 0000.0 0000: 003 02000 000.005.

.00, ..00.:: 00:20 000 05 0. 00.2 00.2 . 00> 02 00> 00> 0.20 00000020. , . , 0:0 00

:80

0000:0000 00 0.. 00.0:0000

00.0300 000 .0880. 20:00:00 00000000000 02:0.

000:. 00 08.0..» 0.00 .0> Scam.

.0000: 00 000000 0.0:00000000: 00000000 0.00 000

.00.. e... £02.09 , .02 . .sz ,. ._ 32; 4.2.2 ._ ; .sz . .sz . . . 050.00 . 1.00.0.2. l. .,_........0 0.8.

00.50 0000050 A room.

$00000 0.0200000 .0> 200000 ._0

0 .0>. .029. 00.000 0.00.005. < 00.2 .002 ..<.z .32 00> Lp.02 00.00 80008.... .0 0803.2
500 3 000... 20 0000: ..

00! 0598000000 0.00.0020. 0000 00000300 0.0““Mwﬁ 000000 :0000:0m 00< ““80.“ 30”“ ..0£:<

800: 05.00.0000: 00:08:.

 

 

00:00:00. 00000.50 «0:00.0— .022... 00 000000 00.00.0080. .0>00... 0:..00 00.00300. 20:000.:Em 00.00.00>

TN 030...

36

So & Morrison 2003; Weber & Roehl 1999). Recently, research has shown that
profiles of the average adult American lntemet user are becoming similar to the
proﬁles of the average American (Pastore, 2001a). lntemet users are becoming
more mainstream, since more people with older age, moderate incomes and
moderate educational backgrounds use the Internet (Pastore, 1999). Joines,
Scherer, and Scheufele (2003) concluded that online information searchers’
demographics continue to move toward the general population, and online

' purchasers' demographics are becoming more similar to online information
searchers.

Even though lntemet users’ demographic proﬁles have been studied,
relatively little academic research has studied how national culture affects the
way people plan vacations and purchase travel products (Money & Crotts, 2003).
One possible reason is that researchers who studied online travel behaviors may
think national citizenship has provided little value in explaining tourist behavior
and attitudes (Crotts & Litvin, 2003). The role of national cultural characteristics
in affecting tourist behavior has been investigated in cultural behavior, consumer
satisfaction, and international marketing studies. In these studies, researchers
have tried to empirically discover what, if any, differences actually exist in the
behavior of tourists of various nationalities (Pizam & Sussmann, 1995).
Researchers found an individual’s cultural belonging and heritage affect the way
in which people experience and interpret tourism goods and services, but it is
also likely to inﬂuence decisions regarding choices of vacations and destinations

(Weiermair, 2000). Crotts and Erdmann (2000) and Crotts and Litvin (2003)

37

 

found national cultural differences are one of many factors inﬂuencing consumer
decision making. Assigning participants’ national culture measure, based on
their country of residence, provides a more robust means to account for cultural
differences than does either country of birth or country of citizenship (Crotts &
Erdmann 2000; Crotts & Litvin 2003).

For national destination marketing organizations, understanding travelers’
different behaviors by national culture is signiﬁcant for international marketing.
Traditional research and marketing have treated American and Canadian
travelers as being similar. They live in the same continent (North America), have
similar immigration histories, and use the same language, English. Cultural
difference studies on travel behaviors have generally been conducted between
Americans, Asians, and Europeans. A few studies were found that examined
differences in travel behaviors between American and Canadian travelers.
Woodside and Jacobs (1985) found that travel behavior, socio-economics, and
beneﬁts-realized between American and Canadian visitors to Hawaii differed
substantially. Canadian visitors most often reported rest and relaxation as the
major beneﬁt realized on their Hawaii visits, while mainland American visitors
reported cultural experiences as major beneﬁts realized (Woodside & Jacobs,
1985). Canadian outbound travel accounts for about 20 percent of total national
person-trips (Statistics Canada, 2001), while in the U.S., outbound tourism
accounts for only ﬁve percent of total national person-trips (U.S. Department of
Commerce, 2001). Even before September 11, Canadian destination marketing

organizations were aware that American travelers have relatively low levels of

38

interest in traveling internationally (Smith 8. Xie, 2003). Studies on Internet use
have shown differences between US and Canada residents. Canada residents
spend more time surﬁng on the web than US residents (Pastore, 2000b).
Canadian web surfers spent nearly 30 minutes per month more online than
American web surfers (Table 2-2) (Pastore, 2000b). Canada had the second
highest share of global e-commerce revenues after the US. in 1998, but,
currently, per capita lntemet retail spending in Canada is CA $23, approximately
half the US. rate of US $41 (Jones, 2003). The body of this research suggests
country of residence is an important demographic variable for studying online

travel behaviors.

 

 

 

Table 2-2

Average Internet Usage in the U.S. and Canada durinﬂay 2000
US Canada

Number of unique sites visited 10 18

Page views per month 662 726

Time spent per monthal 9:05:24 9:34:60

Duration of a page vieweda 0:00:50 0:00:47

Source: Nielsen/[Net Ratings

Retrieved from

http:/Mwwclickzcom/statslbig picture/geographics/article.aha/403541

a. hour(s):minute(s):second(s)

Travel Experience and La test Trip Behavior
Few studies have been published on travel-related behaviors of online
travelers (Morrison et al., 2001). Bonn and colleagues (1999) concluded that
lntemet users who search for travel information were more likely to stay in
commercial lodging establishments and spend more money each day while
traveling. Xu (1999) found a signiﬁcant difference in travel expenditures, with

online purchasers tending to spend more on travel than ofﬂine purchasers.

39

Morrison and colleagues (2001) found that people who traveled to other
countries in the past 12 months were more likely to be online purchasers than
those who did not travel abroad.

Thirty or more years of travel information search studies has shown there are
many other variables (e.g., travel experience and latest trip behavior such as
length of stay of the latest trip and destination of the latest trip) to examine to
understand signiﬁcant relationships between travel information search (or
purchase behaviors) and travel-related behaviors. Travelers who seek the
greater volume of information stay longer in a destination (Fodness & Murray,
1999), visit more attractions (MacKay, 2001/02), travel longer distances, take
longer vacations, or visit new and unfamiliar destinations (Etzel 8. Wahlers 1985;
Gitelson & Crompton 1983). Most of these studies have examined correlations
between variables, not causal relationship. A traveler might be staying longer
because information search revealed more things to do, or it is equally likely that
the anticipation of a longer trip stimulated more information search (Fodness &
Murray, 1999). Thus far research hasn’t shown which situation is more common
or likely.

Seasons of travel, such as spring, summer, fall and winter, may also affect
travel information search and purchase behaviors because different
temperatures, weather, events and so on cause different needs or risks. For
example, Christmas (winter) and Thanksgiving (fall) could contain more family-
oriented events compared to spring break (spring). Visiting family or relatives

may require less travel information search than visiting the Caribbean beach.

40

 

 

General lntemet Use and Internet Use for the Latest trip

A growing literature on general online behavioral characteristics shows the
dynamic nature of information search and product purchase. Weber and Roehl
(1999) found travelers who purchased online have more years of lntemet use
experience (one year or more) and more browser use per week (4 hours or
more) than travelers who did not purchase online. Bonn, Furr, and Susskind
(1999) found people who used the lntemet to seek travel information were likely
to be computer users, purchase travel products online, and search information
for their destination. Other studies have also shown online purchasers spend
more time online per week than those purchasing ofﬂine (Morrison et al. 2001;
Xu 1999). Weber and Roehl (1999) found that online credit card use and
possession of a credit card signiﬁcantly affect online purchase. Speed of Internet
connection has inﬂuenced lntemet use. High-speed lntemet users tended to
spend more time online than their dial-up counterparts (Pastore, 2001b). Pastore
(2001b) found lntemet users who switched from modern (dial-up) to high-speed
(Broadband) increased their number of web pages viewed, number of pages per

person, and time spent online (Table 2-3).

41

 

Table 2-3
The Inﬂuence of Broadband (High-aged) Internet Usage in the U.S.
Before Broadband After Broadband

 

(January 2001) (July 2001)
Page views 2.4 billion 5.5 billion
Pages per person 757 1,170
T'me Spent °"""° 96' 12:21 :50 15:14:00

person during a month’1
Source: Nielsen/INet Ratings
Retrieved from

ht_tp://www.clickz.com/stats/marketslbroadband/article.ghg/10099 870841

a. hour(s):minute(s):second(s)

Few researchers have studied online information search and purchase on an
actual trip (e.g., planning horizon for lntemet use for an actual trip and time spent
online for an actual trip planning) as an element that signiﬁcantly inﬂuences travel
information search and travel product purchase behaviors. As Internet users
engage in every online activities, such as searching for information and
purchasing goods, booking airline tickets, checking local entertainment
information, communicating with business associates, trading stock and banking
online, Internet users have developed their own online strategy for each purpose
(Pastore, 1999). Hence, online information search and online purchase
behaviors are different by lntemet users’ different purpose(s). For example,
websites offering books and magazines, apparel and jewelry, and computer
software and music were more often visited for online purchasing; however,
travel websites and automotive commodity websites were more often visited for
online information search (Ellison, Earl, & 099, 2001). Thus, goal-oriented
lntemet use (e.g., lntemet use for a speciﬁc trip) would be likely to affect travel

information search and purchase behaviors differently.

42

 

Summary I
This review of literature was organized into ﬁve sections, including this
summary. The ﬁrst and second sections suggested that travelers have used the
lntemet differently for travel information search and product purchase. The
theory of case-based vacation planning, discussed in the third section, was
applied to online travel environments. This theory suggests that travel behaviors
differ by the three stages of travel (pretrip, trip and post-trip) and by travel
products. The last section reviewed elements that affect online travel information

search and online travel product purchase behaviors.

43

CHAPTER 3
METHODOLOGY
The problem statement of this research was to understand online travel

planning strategies during the pretrip stage for a general population sample of
lntemet users, and to examine which factors signiﬁcantly encourage or
discourage online travel information search and travel product purchase during
pretrip planning. The conduct of the study included the following organizational
steps: (1) secondary data analysis, (2) procedures for targeting and gathering

data, (3) instrumentation, (4) model evaluation, and (5) a summary.

Secondary Data Analysis
This study was based on secondary data analysis using existing data

collected by the Canadian Tourism Commission (CTC) and SECOR, a Canadian
consulting ﬁrm. Trochim (2001) deﬁnes secondary data analysis as “making use
of an already existing data source.” Secondary data analysis typically refers to
the re-analysis of quantitative data rather than text, which means the secondary
data analysis is used for a new research question or an alternative perspective
on the original question (Hinds, Vogel, & Clarke-Steffen 1997; Szabo & Strang
1997; Trochim 2001). The direction of this research was different from the CTC’s
original study. This thesis focused on studying online travel planning behaviors,
especially during the pretrip, while CTC reported results on consumers’ website

usage for travel from pretrip to post-tn'p.

44

 

There are some advantages to secondary data analysis. Secondary data are "
less expensive for the researcher than conducting a new study and are faster as
the data are already collected and keyed (Trochim, 2001). The most important
merits of secondary data analysis are efficiency as the existing data is recycled
and there can be replication of the prior research ﬁndings (Trochim, 2001). In
most research studies, data that might have taken months or years to collect are
only examined once in a relatively brief way and from one analyst’s perspective
(T rochim, 2001). Thus, there is a potential for error by the original analyst(s) who
tend to approach the analysis from their own perspective using the analytic tools
with which they are familiar (Trochim, 2001). Through different data analyses,
this study replicated some parts of the original study and also showed new
research ﬁndings.

This research has some potential errors or limitations in using secondary
data. The data were conducted in 2001, and accordingly, it may not represent
current travel planning trends. The original physical survey questionnaire and
coding book were not available, instead just a report review. There may be
misunderstanding on interpreting the survey questions or the data analysis. The
secondary data analysts were not involved in data collection and data
management, therefore, judgment on sampling procedures, reliability and validity
of the data gathering instruments and procedures, and treatments and methods
of the collection of data cannot be made in this research. To overcome these
problems, original research reports were reviewed, and the primary researcher,

Mr. Scott Meis, was consulted.

45

 

Procedures of Original Data Collection and Management ‘

The original research used a three-phase data collection effort. The ﬁrst
stage, not of central interest in this research, was focus groups conducted with
Canadian citizens in Toronto on July 26"“ 2001. The second stage used a
telephone survey instrument to contact 1,600 North American travelers (800 from
each country) with lntemet access between November 1"t and November 10‘“,
2001. For the third stage, a total of 21 ,600 invitations were e-mailed to North
American Internet users (Table 3-1). Between November 8th and December 18‘",
2001, 5,665 surveys were started and 2,470 surveys were completed online.
The overall response rate was 11.3 percent. The lntemet survey results are
reliable within 3.1 percent at 95 percent conﬁdence level. The general population
of American adults (18 years old or more) is 8.7 times greater than Canadian
adults (Table 3-1). However, the number of respondents who completed the
survey was evenly distributed.

Table 3-1: Population and Number of Survey Respondents

 

USA Canada
Population“ 204.9 millionb 23.7 million°
Sample p0pulation 21,600
Number of respondents
who completed the 1,337 (54%) 1,133 (46%)
survey

 

a. Population of those whose age is 18 years old or more
b. U.S. Census Bureau (2001 Supplementary Survey Summary Tables)
c. Statistics Canada (Population as of July 24, 2001)

The survey respondents represent US residents (54 percent of responses)
and Canada residents (46 percent), whose age is 18 years old or more. They
had web access, were willing to complete the online instrument, reported web

usage for travel or vacations (e.g., planning, researching, reserving, or paying by

46

credit card for travel products), and took at least one vacation in the past 12
months between November 2000 and October 2001. The primary data collection
instrument for the lntemet survey was a self-administered online questionnaire
with 95 questions. Twelve demographic questions were included at the
beginning of the web-survey. Many questions were focused on the most recent
trip of one or more nights at least 50 miles (80 km) away from home. The
purpose of trips taken included vacation, leisure or get-away trips, and excluded
business or commuting to and from school or work or trips to a cottage or
vacation home that respondents regularly use. For some questions, particularly
behavioral ones, respondents were offered “not applicable” as an answer and
other questions offered “don’t recall.” Thus, the number of “valid” respondents
for each question varies. The original researchers adjusted denominators to
include only those respondents who were in the market to search for information
or purchase a travel product. Canadian data were weighted by the primary data
researchers on education and marital status to better represent the national

demographic proﬁle.

Instrumentation
As a result of the literature review, explanatory variables were added to
understand those elements that encourage or discourage online travel
information search and online travel product purchase behaviors. As shown in
Table 3-2, there are three measurement dimensions: general background, travel

planning behavior during pretrip, and trip speciﬁc behavior during trip. General

47

 

Table 3-2
Constructs and Associated Dimensions Included in the Analysis

Constructs and Associated Number of Items and

 

Dimensions Contents Measurement
General Background:
Tourists’ Demographics 4 items
Country of residence/ Gender/ Nominal/
.. “Age/Education.“ . ._ Ordinal
Travel Experience 1 item
Number of vacation trips in .
._ . recent 12 _months Ordinal
General Internet Use 4 Items

Years of Internet use/ Time
spent online per week/ Speed Nominal/
of Internet connection/ Credit Ordinal
_ Vsarduseforonlinepurchase
Travel Planning Behavior During Pretrip:
Internet Use for the Latest 2 items
Tnp Planning horizon for lntemet
use for the latest trip/ Time Ordinal
‘_ K .,spentonlineforthelatesttrip
Travel Information Search 6 items

for the Latest Trip Accommodations! Activities/
Attractions/ Car rentals/ Nominal
. .Kﬂmevents/Flights.
Travel Product Purchase for 5 items
the Latest Trip Accommodations/ Activities!
Attractions/ Car rentals/ Nominal
V, I _ .1 ,. Events/Flights
Trip Speciﬁc Behavior During Trip:
Latest Trip Behavior 3 items

Length of stay of the latest trip/ .
Destination of the latest trip/ 333:2?"
Season of the latest trip

48

background has three types of variables: tourists’ demographics including
country of residence, gender, age and education; travel experience including
number of vacation trips in recent 12 months; and general Internet use including
years of lntemet use, time spent online per week, speed of lntemet connection
and credit card use for online purchase. Travel planning behavior during pretrip
has three types of variables: lntemet use for the latest trip including planning
horizon for lntemet use for the latest trip and time spent online for the latest trip;
travel information search behaviors on six travel products, accommodations,
activities, attractions, car rentals, events and ﬂights; and travel product purchase
behaviors on six travel products, accommodations, activities, attractions, car
rentals, events and ﬂights. Trip speciﬁc behavior during trip is represented by the
latest trip behavior including length of stay of the latest trip, destination of the
latest trip and the season that the latest trip.

The original data selected for this research have ordinal or nominal
measurement scales (Table 3-1 and Appendix 1). Some variables were modiﬁed
from their original data form. Travel information search variables and travel
product purchase variables are nominal scales with four category responses
including “online,” “ofﬂine,” “both online and ofﬂine” and “not applicable” (N/A).
Survey respondents’ choice of MA may be interpreted in several ways. For
example, in searching for information on accommodations during the pretrip,
respondents may choose N/A because: they did not search for information on
accommodations but searched for another product(s) during pretrip; they did not

search for information at all but purchased at least one travel product during

49

 

 

pretrip; they did not search for information on accommodations during pretrip but ‘
searched for information on accommodations during trip or after trip; they did not

search for information on accommodations during pretrip but purchased

accommodations during trip; they did not remember if they searched for

information on accommodations during pretrip; or they did not understand what

the survey question meant. In this research, N/A is interpreted that respondents

did not search for information on a speciﬁc travel product during pretrip.

However, respondents reported some other information search or product

purchasing before departing. For data analysis, the name “both online and

ofﬂine” was changed to “on/ofﬂine” and “MA” to “none.”

Model Evaluation

The purpose of model evaluation was to test the signiﬁcance of relationships
between variables in three dimensions such as general background, travel
planning behavior during pretrip, and trip speciﬁc behavior during trip. The ﬁrst
step of model evaluation was to compare the data using descriptive statistics,
speciﬁcally cross-tabulation. The second step was to evaluate the model
outlined in Figure 1-2 using tests of signiﬁcance (Chi-square) and measures of
association (Contingency Coefﬁcient). To provide details of association in
variables, cross-tabulation was also used.

For testing hypotheses, the data were initially examined by country of
residence (US and Canada), and the statistical results were compared. As

reviewed in Chapter 2, country of residence has been shown to be a signiﬁcant

50

independent variable in travel information search and travel product purchase
behaviors. From the viewpoint of national destination marketing organizations,
understanding travelers’ different information search and purchase behaviors by
national culture (speciﬁcally country of residence) is important in their target
marketing. Prior to hypothesis testing, different travel information search and
travel product purchase behaviors across six travel products were examined and
compared through cross-tabulation and Chi-square tests.

Descriptive statistics is a method for presenting quantitative descriptions in a
manageable form (Babble, 1998). It is convenient to describe single variables or
the association between variables. Developing tables of cross-tabulation,
speciﬁcally with percentages, is the most commonly used technique to
summarize the data in hypothesis tests. However, the results in a table are not
always clearly understood and sometimes seem inconsistent (Sirkin, 1999).
Moreover, except for the extremes of perfect relationship and no relationship, it is
not possible to see the exact amount of relationship that exists between the
variables (Sirkin, 1999).

To test Hypotheses one to ten (Figure 1-4), Chi-square ()8) was used for
examining statistical signiﬁcance of difference of relationship between variables.
Chi-square test can be used in most levels of measurement - nominal, ordinal, or
interval (Sirkin, 1999). Since the measurement scale of these data is nominal or
ordinal (Table 3-1 and Appendix 1), Chi-square was a good technique to test the
hypotheses. According to Babble (1998),

Chi-square is based on the null hypothesis test: the assumption that there is
no relationship between the two variables in the total population. Given the

51

observed distribution of values on the two separate variables, the conjoint

distribution is computed to examine if there were no relationship between the

two variables. The result of this operation is a set of expected frequencies for
all the cells in the contingency table. As comparing this expected distribution
with the distribution of cased actually found in the sample data, the probability
that the discovered discrepancy could have resulted from sampling error

alone (p.427).

Rejecting a null hypothesis means that the Chi-square value obtained was not
the result of sampling error, but instead reﬂected that the variables in the
population are Indeed related (Sirkin, 1999). For the decision on whether or not
to reject the null hypothesis, the obtained Chi-square is compared to the critical
value of Chi-square (p value) at the .05 level (Sirkin, 1999). Chi-square has the
following limitations. First, the Chi-square test may cause a sampling error or an
inverse function of sample size (Babble, 1998). In larger samples, there is less
chance that the correlation could be simply the product of sampling error (Babble,
1998). Second, the Chi-square test does not show how each variable is
signiﬁcantly different from each other, although it tests signiﬁcance of difference
between variables. To resolve this problem, cross-tabulation was concurrently
tested to provide detail patterns through the frequency and percentage.

Contingency Coefﬁcient analysis was used to test Hypothesis 11 (Figure 1-2)
to compare the magnitude of association between travel information search and
travel product purchase across six travel products. This estimation is appropriate
for nominal data with more than two groups or categories of responses,
particularly when symmetry between the variables is present (Sirkin, 1999).

While Chi-square analysis shows the signiﬁcance of association, the actual value

of the Chi-square statistic and its associated observed signiﬁcance level provides

52

 

little information about the strength and type of association between two i'
variables (Norusis, 2002). In addition, Chi-square results are highly dependent
on the sample size, the number of rows and columns in the table, and the extent
of the departure from independence (Norusis, 2002). Contingency Coefﬁcient
estimation modiﬁes the Chi-square statistic so that it is not inﬂuenced by sample
size. A Contingency Coefﬁcient estimate ranges from zero (0) to one (1 ), with
zero (0) corresponding to no association and one (1) to perfect association:
however, the coefﬁcient does not go as high as one (1 ), even for a table showing
what seems to be a perfect relationship. The largest value it can have depends
on the number of rows and columns in the table: therefore, it can never be used
to compare tables of different sizes (Norusis, 2002). However, the Contingency
Coefﬁcient measure is difﬁcult to interpret. Although it can be used to compare
the strength of association in different tables, the strength of association being
compared is not easily related to an intuitive concept of association (Norusis,

2002)

53

 

Summary i
Secondary data analysis discussed the advantages and disadvantages of this
research methodology. Procedures of the original data collection and
management were explained. The instrumentation section explained the
constructs, and associated dimensions included in this analysis. This section
also explained which survey questions were chosen from the original data and
how new scales were developed from the original data. Model evaluation
described the strategies used for testing the hypotheses. Descriptive statistics,
Chi-square tests and Contingency Coefﬁcient analysis were discussed for

hypothesis testing.

 

CHAPTER 4
RESULTS

The problem statement of this research was to understand online travel
planning strategies during the pretrip stage for a general population sample of
lntemet users, and to examine which factors signiﬁcantly encourage or
discourage online travel information search and travel product purchase during
pretrip planning. The analyses of the data are presented in this chapter
according to the following topics: (1) description of the respondent demographic
proﬁle and (2) results of hypothesis testing. Results of hypotheses testing were
separated into two parts, hypotheses 1 to 10 and hypothesis 11. Each part

includes a summary.

Description of the Respondent Demographic Proﬁle
Prior to hypothesis tests, which were aimed at understanding which elements
are signiﬁcantly related to travel information search and product purchase
behaviors, a study of demographic proﬁles of US and Canada respondents was

necessary to determine differences from the general population.

US Respondents
A demographic proﬁle of US respondents is presented in Table 4-1 with US
population data from the 2001 Census. Females were more likely to respond

than males, commonly found in survey research, and they were

55

 

Table 4-1
US Respondent Demographic Proﬁles

 

 

Proﬁles Survey Sample Population‘
n % n %
Gender
Male 546 40.9 98.3b 48.0
Female 788 59.1 105.5” 52.0
Age
18 to 24 41 3.1 24.9 12.2
25 to 29 104 7.8 18.3 8.9
30 to 34 148 11.1 20.2 9.8
35 to 39 158 11.8 21.9 10.7
40 to 44 193 14.5 22.6 11.0
45 to 49 188 14.1 20.5 10.0
50 to 54 198 14.8 18.2 8.9
55 to 59 137 10.3 13.9 6.8
60 to 64 92 6.9 11.1 5.4
65 to 70 53 4.0 94" 4.6
.. 71.9r_older,., _ 22,“, 1-6 23-9d 11-6
Educatisn, , , _ w ,. ,. ‘
Less than high school 6 0.5 N/A" N/A"
Some high school 23 1.7
Graduated high school 149 11.2
Some university/college!
technical college 395 29’6
Graduated university/college
[technical school 440 33'0
_. .POSIQFaduaiemw... H .320 . .24-0. M
Annual Household Income'
Less than $15,000 52 4.0 16.7 15.6
$15,000 to 24,999 88 6.8 13.7 12.9
$ 25,000 to 39,999 204 15.8 19.6 18.4
$ 40,000 to 59,999 278 21.5 20.7 19.5
$ 60,000 to 99,999 378 29.3 22.2 20.9
$ 100,000 to 149,999 172 13.3 8.7 8.2
$ 150,000 to 199,999 69 5.3 2.5 2.3
$ 200,000 or more 53 4.1 2.3 2.2

 

a. U.S. Census Bureau: 2001 Supplementary Survey Summary Tables (unit: millions of people)
b. Population of those whose age is 18 years old or more

0. Data were available in 65 to 69 year segment

d. Data were available in 70 years or older segment

e. NIA: not available

f. US dollar

56

 

overrepresented in the sample when compared to US population of female adults
(18 years old or more). Over half of the respondents (55 percent) were between
35 and 54 years old. Twenty-two percent were young adults of 34 years or
younger, and 23 percent were 55 years and older. Thirty to 64 year olds were
overrepresented, and 18 to 24 year olds and 71 or older were underrepresented
in the sample compared to the US population of adults. Six out of ten
respondents (57 percent) held college or graduate degrees. Only a few
respondents did not have high school degrees. About a half of the respondents
(51 percent) had an annual household income of US $40,000 to $99,999.
Respondents who had an annual household income of US $40,000 or more were
overrepresented, and respondents with less than US $40,000 were
underrepresented in the sample compared to the US population of annual

household income.

Canada Respondents

A demographic proﬁle of Canada respondents is presented in Table 4-2 with
Canadian population data from the 2001 Statistics Canada. Females were more
likely to respond than males, and they were overrepresented in the sample when
compared to the Canadian population of female adults (18 years old or more).
But the gap of overrepresented rate is smaller than US residents (Table 4-1 and
4-2). Over half of the Canada respondents (54 percent) were between 35 and 54
years old. Twenty-six percent were young adults of 34 years or younger, and 20

percent were 55 years and older. Twenty-ﬁve to 64 year olds were

57

Table 4-2
Canada Respondent Demogaphlc Proﬁles

 

 

Proﬁles Survey Populationa
n % n %

Gender

Male 489 43.3 11.6” 48.9

Female 641 56.7 12.1 b 51.1
Age

18 to 24 35 3.1 2.2 9.2

25 to 29 133 11.8 2.1 8.9

30 to 34 125 11.1 2.2 9.4

35 to 39 139 12.3 2.5 10.5

40 to 44 168 14.9 2.7 11.5

45 to 49 156 13.8 2.5 10.6

50 to 54 142 12.6 2.2 9.2

55 to 59 122 10.8 1.8 7.8

60 to 64 69 6.1 1.4 5.9

65 to 70 29 2.5 1.1 ° 4.8
. .710'9l96t ‘ w . V. 1.1 10 .2-9" “.12-3. ‘
Education- ,_ . . _ ..... . .. _ _ _

Less than high school 5 0.4 N/A° N/A’

Some high school 40 3.5

Graduated high school 162 14.4

Some university/college!

Technical college 198 17'6

Graduated university/college/

Technical school 613 54'3

Post graduate 1 1 1 9.8
Annual Household Income'

Less than $15,000 41 3.7 NIA° WA"

$15,000 to 24,999 81 7.3

$ 25,000 to 39,999 155 14.1

$ 40,000 to 59,999 244 22.1

$ 60,000 to 99,999 358 32.6

$ 100,000 to 149,999 139 12.6

$ 150,000 to 199,999 52 4.7

$ 200,000 or more 31 2.8

 

a. Statistics Canada: Population as of July 24, 2001 (unit: millions of people)
b. Population of those whose age is 18 years old or more

c. Data were available in 65 to 69 year segment

d. Data were available in 70 years or older segment

9. N/A: not available

f. Canadian dollar

58

 

 

overrepresented, and less than 24 year olds and 65 to older were I
underrepresented in the sample compared to the Canadian population of adults.

Six out of ten respondents (64 percent) held college or graduate degrees. Over

half of the respondents (54 percent) held university/college! technical school

degrees. Only a few respondents did not have high school degrees. Canada

respondents were likely to have higher education compared to US respondents.

About half of the respondents (55 percent) had an annual household income of

CA $40,000 to $99,999.

Results of Hypothesis Testing

This section is separated into three parts (Figure 4-1). The ﬁrst part includes
testing of Hypothesis 1a (country of residence differs on information search
behaviors) and Hypothesis 4a (country of residence differs on product purchase
behaviors). Hypotheses were separately tested with US and Canada data. Prior
to other hypothesis tests, it was necessary to study whether country of residence
signiﬁcantly affects other independent variables, travel information search and
travel product purchase. The second part presents the statistical results for
Hypotheses 1b to 106, except 4a, which examined variables that signiﬁcantly
differ in regard to travel information search and product purchase across six
travel products during pretrip planning. The third part presents the statistical
results for Hypothesis 11 (travel information search behaviors and travel product

purchase behaviors during pretrip are associated, however, not perfectly).

59

To test Hypotheses 1 to 10, cross-tabulation and Chi-square tests were used
to examine statistical signiﬁcant differences on relationships between variables.
To test Hypothesis 11, cross-tabulation and Contingency Coefﬁcient analysis
were used to compare the magnitude of association between travel information

search and product purchase across six travel products.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4-1

Guide Map of Result Tables of Hypothesis Tests

E Tables . *
: 4_3 to 4_7 All other Independent ;
5 and 4-21 variables I
§ (H1a and Country of residence Travel information
5 H4a) search 5
Travel product I
5 purchase 5
{12518; """"" £7.17172'_iiiiiii';77.77;": """""""""""""""""""" ‘5
: 4-8 to 4_20 f Country of reeldence ‘ j: E
5 and 4-21 ‘ ' t ‘ “ " ”' Travel information I
I search 5
E (”W to H100 All other independent * ’ :
5 except H4a) variables 5
I Travel product 5
5 purchase 5
"fillies """"""""""""""""""""""""""""""""""""""""

4-22 to 4-28
(H1 1)

 

 

 

Travel information Travel product
search purchase

a. Shading denotes controlling for country of residence variable.

60

 

Comparisons of US and Canada Samples
Comparisons of US and Canada Samples on General Background

General Background included tourists’ demographics, travel experience and
general lntemet use (Table 4-3). Tourists’ demographics included gender, age
and education. Gender did not differ signiﬁcantly between US and Canada
respondents. Both US and Canada females were more likely to respond than
males. Age also did not differ signiﬁcantly. Over half of US and Canada
respondents were aged 35 to 54 years old. Education differed signiﬁcantly.
Canada respondents were likely to have higher education background compared
to US respondents.

In terms of travel experience, the number of vacation trips in recent 12
months was signiﬁcantly different between US and Canada respondents. US
respondents were more likely to take vacations than Canada respondents.

General lntemet use included years of lntemet use, time spent online per
week, speed of lntemet connection and credit card use for online purchase.
Years of lntemet use was not signiﬁcantly different between US and Canada
respondents. Time spent online per week, speed of lntemet connection and
credit card use for online purchase were signiﬁcantly different. US respondents
were likely to spend more time online per week. More than half of the Canada
respondents (53 percent) had a high speed Internet connection at home,
compared to 40 percent of US respondents. US respondents were more likely to

purchase online with credit cards than Canada respondents.

61

Table 4-3
Comparison of US and Canada Samples on General Background

 

Signiﬁcant
US Canada dlfference by
country of
residence
n % n % f P-Ievel
Tourists’ Demographics
Gender 1.4 .240
Male 546 40.9 489 43.3
.‘ Female .‘ , . “.788... 59.3.1- .. 641 56-7 .
Age 7.8 .101
18-24 41 3.1 35 3.1
25-34 252 18.9 258 22.9
35-44 351 26.3 307 27.2
45-54 386 28.9 298 26.4
SSoroIder __ 304 ..____2_2_.8, ”.231 . 20.4.___ _ _
Education 12.7 .000”
Less than a college
degree 573 43.0 406 35.9
.. 9911696969766. Oi mor 6.. . .760 .. 57.20 .724 .. 54-1 _
Travel Experience
Number of vacation trips In recent 12 months 22.3 .000”
1 185 13.9 193 17.1
2 340 25.5 342 30.3
3 286 21.4 229 20.3
4 194 14.5 163 14.4
. 59mm: ., _‘ 329247 _,203_.__ .130...
General lntemet Use
Years of Internet use 3.6 .314
Less than 2 years 221 16.6 197 17.4
2-4 years 413 31.1 381 33.8
4-6 years 385 29.0 316 28.0
More than 6 years 7 309 23.3 234 20.8
Time spent online per week 19.2 .000”
4 hours or less 312 23.5 344 30.5
5-10 hours 358 27.0 309 27.4
11-20 hours 321 24.2 241 21.3
2,1 _.hou.r39r more . 33.7. _. .. 25.4. 233 . . 207
Speed of lntemet connection 44.7 .000“
High speed
(T1, Cable, ISDN, ADSL) 530 39.8 601 53.2
Regular speed
123330. ‘_ ’9? ‘39-? 5?." 46-8 .
Credit card use for online purchase 117.2 .000“
Yes 1,186 88.9 810 71.7
No 148 11.1 319 28.3

 

" p < .05; " p < .01; and '** p < .001

62

Comparisons of US and Canada Samples on Travel Planning Behavior
during Pretrip

Travel planning behavior during pretrip included lntemet use for the latest trip,
travel information search and travel product purchase. Travel information search
and travel product purchase were tested across six travel products with four
category responses, online, ofﬂine, on/ofﬂine and none. “On/offline” means
respondents searched for information or purchased travel products both online
and ofﬂine during pretrip. “None” means respondents did not search or purchase
during pretrip.

lntemet use for the latest trip included planning horizon for lntemet use for the
latest trip and time spent online for the latest trip (Table 4-4). Both elements
were not signiﬁcantly different between US and Canada respondents.

Table 44

Comparison of US and Canada Samples on lntemet Use During Pretrip for
the Latest Trip

 

Signiﬁcant
us Canada difference by
country of
residence
n % N % f P-Ievel
lntemet Use During Pretrip for the Latest Trip
Planning horizon for lntemet use for the latest trip 5.0 .288
Less than 2 weeks 175 13.3 168 15.0
2 to 4 weeks 255 19.3 227 20.2
1 to 2 months 282 21.4 237 21.2
2 to 4 months 302 22.9 219 19.5
-_ More than 4 monms , . _ 305 g 23.1” 270 24.1
Time spent online for the latest trip 1.1 .582
2 hours or less 495 37.6 433 38.7
3 to 5 hours 420 31.9 335 29.9
6 hour or more 403 30.6 352 31.5

 

* p < .05; ** p < .01; and *** p < .001

63

 

As shown in Table 4-5, US respondents’ most popular product for

l."'

Information search (including online, ofﬂine and on/ofﬂine) for the latest trip
during pretrip was accommodations (86 percent of US respondents), followed by
attractions (74 percent). US respondents’ least popular product for information
search was car rentals (39 percent of US respondents), followed by activities (52
percent). Canada respondents’ most popular product for information search
(including online, ofﬂine, and on/ofﬂine) was also accommodations (88 percent of
Canada respondents), followed by attractions (78 percent). Canada
respondents’ least popular product for information search was car rentals (30
percent of Canada respondents), followed by ﬂights (56 percent).

US and Canada respondents searched for travel information for the latest trip
during pretrip online more than ofﬂine. US respondents searched for information
online on accommodations the most (52 percent of US respondents), followed by
ﬂights (44 percent) and attractions (32 percent). US respondents searched for
information ofﬂine on attractions the most (10 percent of US respondents),
followed by activities (8 percent) and events (7 percent). Canada respondents
searched for information online on accommodations the most (50 percent of
Canada respondents), followed by attractions (34 percent) and ﬂights (29
percent). Canada respondents searched for information ofﬂine on attractions the
most (10 percent of Canada respondents), followed by activities (9 percent) and
events (9 percent).

Respondents’ information search for car rentals and ﬂights was signiﬁcantly

different between US and Canada respondents. US respondents were more

64

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65

likely to search for information online on car rentals and ﬂights than Canada
respondents.

As shown in Table 4-6, US respondents’ most popular product purchased
(including online, ofﬂine and on/ofﬂine) during pretrip for the latest trip was
accommodations (72 percent of US respondents), followed by ﬂights (54 percent).
US respondents’ least popular product purchased was events (24 percent of US
respondents), followed by activities (26 percent). Canada respondents’ most
popular product purchased was also accommodations (71 percent of Canada
respondents), followed by ﬂights (47 percent). Canada respondents’ least
popular products purchased were activities (24 percent of Canada respondents)
and events (24 percent).

US respondents purchased accommodations the most online for the latest
trip during pretrip (34 percent of US respondents), followed by ﬂights (33 percent)
and car rentals (19 percent). US respondents also purchased accommodations
the most ofﬂine (29 percent of US respondents), followed by attractions (20
percent) and activities (17 percent). Canada respondents purchased
accommodations the most online (22 percent of Canada respondents), followed
by ﬂights (15 percent) and car rentals (10 percent). Canada respondents also
purchased accommodations the most ofﬂine (40 percent of Canada respondents),
followed by ﬂights (28 percent) and attractions (24 percent).

Accommodations, attractions, car rentals, events and ﬂights signiﬁcantly
differed in terms of travel product purchases during pretrip for the latest trip

between US and Canada respondents. US respondents were more likely

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67

to purchase accommodations, car rentals and ﬂights online than Canada
respondents. Canada respondents were more likely to purchase

accommodations and ﬂights ofﬂine than US respondents.

Comparisons of US and Canada Samples on Trip Speciﬁc Behavior during
Trip

In terms of trip speciﬁc behavior, latest trip behavior was examined in this
study. Latest trip behavior included length of stay of the latest trip, destination of
the latest trip, and the season of this latest trip (Table 4-7). These three
elements were all signiﬁcantly different between US and Canada respondents.
Canada respondents were more likely to stay longer (7 nights or more) for the
latest trip than US respondents. Four out of ten of US respondents stayed for
three to six nights for the latest trip compared to 29 percent of Canada
respondents. Canada respondents were more likely to travel out of North
America than US respondents. US respondents were likely to travel out of state,
but within the U.S. Almost half of Canada respondents (48 percent) traveled out
of Canada, while 35 percent of US respondents left the U.S. US respondents
were more likely to travel during the fall than other seasons for their vacation.
Canada respondents were more likely to travel for their vacation during the

summer than other seasons.

68

Table 4-7
Comparison of US and Canada Samples on Trip Speciﬁc Behavior During
Trip

 

Signiﬁcant
US Canada difference by
country of
residence
n % n % f P-Ievel
Latest Trlp Behavior
Length of stay of the latest trip 42.6 .000“
Weekend/short
(1 to 2 nights) 168 12.6 138 12.3
Up to 1 week
(3 to 6 nights) 524 39.3 327 29.0
2 weeks
(7 to 13 nights) 401 30.1 355 31.5
14 nights or more 241 g 18.1 308 27.3
Destination of the latest trip 64.4 .000***
Within province/state 286 21.4 259 23.0
Out of province/state, within
country 576 43.2 323 28.7
Out of country, within North
America 290 21.7 303 26.9
_.._QUt.0fN0rthAmerica . .. H.182 _ 13.6. ”242. ”221-5 .. ,
Season of the latest trip 20.0 .000'”
Winter 84 6.3 97 8.6
Spring 181 13.6 158 14.0
Summer 469 35.2 460 40.8
Fall 600 45.0 413 36.6

 

* p < .05; '* p < .01; and *** p < .001

69

—1
A Inn“;

 

Travel Informa tion Search and Travel Product Purchase

Travel lnformatlon Search and Travel Product Purchase Inﬂuenced by
Tourists’ Demographics

Gender signiﬁcantly differed in regard to information search on
accommodations, activities, attractions, car rentals and ﬂights in the U.S., and
events and ﬂights in Canada (Table 4-8). US male respondents were more likely
to search for information (including online, ofﬂine and on/ofﬂine) on
accommodations, activities, car rentals and ﬂights than US female respondents.
US male respondents were more likely to search for information on/ofﬂine on
accommodations and activities and online on car rentals and ﬂights than US
female respondents. US female respondents were more likely to search for
information online on attractions than US male respondents. Canada female
respondents were more likely to search for information (including online, ofﬂine,
on/ofﬂine) on events than Canada male respondents. Canada female
respondents were more likely to search for information online on events than
Canada male respondents. Canada female respondents were more likely to

search for information on/ofﬂine on ﬂights than Canada male respondents.

70

Table 4-8
Travel Information Search and Travel Product Purchase Inﬂuenced by

 

 

Gender
Travel Information Search Travel Product Purchase
Gender : Online Ofﬂine On/ofﬂine None 1 Online Ofﬂine On/ofﬂine None
I Accommodations
us Male 50.2% 6.6 32.7 10.5 35.7% 29.3 9.0 26.0
Female 52.8 5.3 25.3 16.7 32.7 28.3 9.7 29.3
, x’=16.2.p=.001*f _ _‘ x’=2.4.p=.496 .
CA Male? 51.6% 6.1 31.8 10.5 ‘ 25.4% 38.1 10.5 26.0
Female 48.9 9.3 28.9 12.9 18.5 40.6 8.9 32.0
a x560. P.=.-.1 .14 : x2‘=10.-6.P= .014" .
ZActletles
us Male 24.7% 9.8 22.7 42.8 5.7% 19.0 4.0 71.2
Female; 23.1 6.5 19.3 51.1 4.2 15.9 3.6 76.3
.. x’,=11.o‘..p=,3012',V.A,“ ;x’=4,.e.p=.202,
CA Male? 25.7% 9.0 20.5 44.8 ' 2.0% 19.3 3.7 75.0
Female; 25.0 9.8 23.0 42.2 3.6 16.8 2.5 77.0
E {4:14, p= .712 x2=4.6, p= .201
Attractions
us Male; 27.9% 11.8 35.1 25.3 4.9% 19.6 4.8 70.7
Female 34.4 8.3 31.2 26.1 4.7 20.8 4.2 70.2
.. x2=98p=021 :x’=o.5.pi=..919 A V
CA Maleé 32.9% 9.4 34.9 22.8 3.1% 23.7 4.7 68.5
Female; 34.7 10.5 33.6 21.1 : 2.7 24.0 2.4 71.0
12:11, p: .772 ; 38:49, p= .177
:Car Rentals
us Male, 28.4% 6.5 9.2 55.9 24.5% 11.9 2.0 61.5
Female? 23.6 4.1 8.2 64.1 i 15.3 9.1 2.9 72.6
. ,, ._x’s_13.._z._ps.o_17* __ _....._..x??23-6.P=000*“
CA Male? 18.6% 6.4 5.9 69.1 12.7% 11.9 1.6 73.8
Female; 13.5 7.9 7.7 70.9 ; 7.2 13.7 1.9 77.2
‘ x2=6.9, p= .077 : 38:99, p= .020*
Events
us Male; 26.2% 8.1 21.6 44.1 6.2% 13.7 3.5 76.6
Female: 28.5 5.4 20.3 45.8 g 6.9 13.9 3.6 75.6
. We.W...x.2,=4-7..pee.19.2- ......... e;x.'"l=0-3..Ap=..-.963 ‘ ‘
CA Male; 24.2% 8.4 18.9 48.5 3.9% 15.8 3.3 77.0
Female 30.7 9.3 21.4 38.6 4.5 17.6 2.7 75.2
1’3113. let-1.003;: ....... tie-+1.2. p= .742
,Flights
us Male? 46.9% 5.5 14.8 32.8 35.9% 17.6 4.9 41.6
Female: 42.0 4.2 13.0 40.8 N 31.6 14.1 5.1 49.2
.V.;__Ax’=9..0.p_=..0232 AAAAAAAAA _x’=8.5..pl=h.037* _ ,.
CA Male; 31.3% 9.4 14.1 45.2 15.6% 24.8 5.7 53.8
Female; 27.6 8.3 21.6 42.5 _, 14.0 29.4 4.1 52.5
2 12:10.5, p= 015* : 18:42, p= .236

 

* p < .05; ** p < .01; and *"p < .001

71

Tourists’ gender signiﬁcantly differed in regard to purchasing car rentals and
ﬂights in the U.S., and accommodations and car rentals in Canada (Table 4-8).
US male respondents were more likely to purchase car rentals online than US
female respondents. US female respondents were less likely to purchase ﬂights
than US male respondents. Canada male respondents were more likely to
purchase accommodations and car rentals online than Canada female
respondents.

Age signiﬁcantly differed in regard to information search for
accommodations, activities and ﬂights in the U.S., and accommodations,
activities, events and ﬂights in Canada (Table 4-9). Younger US respondents (18
to 24 years old) and older respondents (55 years or more) were less likely to
search for information online on accommodations. Younger US respondents
were likely to search for information ofﬂine on accommodations. Older US
respondents were likely to search for information on/ofﬂine on accommodations.
US respondents who were younger were more likely to search for information
online on activities. Younger US respondents were less likely to search for
information online on ﬂights, but more likely to search for information on/ofﬂine
than other age groups. Younger Canada respondents were less likely to search
for information online and on/ofﬂine on accommodations. Canada respondents
who were younger were more likely to search for information online on activities
and events. Younger Canada respondents were more likely to search for

information on/ofﬂine on ﬂights than other age groups.

72

Table 4-9
Travel Information Search and Travel Product Purchase Inﬂuenced by Age
Travel Information Search Travel Product Purchase

 

Age Online Ofﬂine Sfrﬂfne None : Online Ofﬂine 3% n e None
Accommodations
us 1824 ' 37.5% 25.0 27.5 10.0 19.5% 39.0 7.3 34.1
25-34 50.6 8.0 23.7 17.7 31.9 29.5 7.2 31.5
35-44 60.6 4.0 24.4 10.9 36.3 27.7 10.6 25.4
45-54 52.3 3.4 31.5 12.8 f 35.4 30.5 9.9 24.2
55 or older 43. 3 6. 7 32.7 17.3 33.1 25.5 9.6 31.8
. x =59.7, p=_ .0003? g 78:15.0, p= .241
CA 18-24 34.3% 11.4 20. 0 34.3 , 25.0% 30.6 0 44 4
25-34 46. 5 9.3 28. 3 15.9 4 18.3 44.0 8.2 29. 6
35-44 51.3 8.2 32.4 8.2 20.3 39.2 11.1 29.4
45-54 52.9 5.4 31.3 10.4 21.9 39.4 9.1 29.6
55 or older x 521.1 9. 2 29.7 10.0 3 26.1 36.5 10.9 26. 5
. x =32. 4, p- 001" 12:15.1, p= .235
, Activities
us 18-24 30. 0% 10. 0 30.0 30.0 4.9% 29.3 4.9 61.0
25-34 29.7 7.6 22.9 39.8 6.0 20.7 2.8 70.5
3544 j 26.1 9.5 23.3 41.1 4.6 18.9 4.9 71.7
45-54 ,~ 22.4 7.8 21.9 47.9 5.2 16.9 4.9 72. 9
550rolder,‘ 17.0 6.0 13.0 64.0 3.6 10.9 1.7 83.8
....x =521P= 00.0”” .N .x =269P=_ 008“ .
CA 18-24 36.1% 13. 9 16. 7 33.3 0% 25. 7 2.9 71.4
2534 30. 7 10. 5 24. 9 33.9 3. 5 23. 4 4.3 68.8
35-44 27.7 9.8 24.8 37.8 5.5 19.2 2.3 73.0
45.54 , 24.2 6.7 21.5 47. 5 ’ 1.3 15.8 2.4 80.5
55 or older 15. 8 10. 5 15.8 57. 9 , 1.3 11.7 3.5 83. 5
.1: ’.=44 5 p.= 5053*. x2=31-,7.P= .0025
AttraCtIOns
us 18-24 15.0% 12.5 45.0 27.5 4.9% 26.8 4.9 63. 4
25-34 29.7 11.2 29.7 29.3 4.4 22.3 4.0 69. 3
35-44 x 35.6 10.6 31.3 22. 4 6.0 22.9 4.3 66. 9
45-54 ‘ 31.5 9.1 35.9 23.4 3.6 19.5 5.7 71.1
55 or older : 321.3 8. 0 31.3 29. 3 . 5.3 15.9 3.3 75.5
'x =17..0 p= .150 , ;x2=12-4. p= .416
CA 18-24 : 50. 0% 8. 8 20.6 20.6 , 0% 37.1 0 62.9
25-34 E 35. 8 7. 8 34.2 22.2 1.9 24.9 4.3 68.9
35-44 ~ 35.0 10.1 35.3 19.6 2.9 30.4 2.9 63.7
45.54 33.1 10.1 37.5 19. 3 3.7 21.1 1.7 73. 5
55 or older 29. 3 12. 7 30.6 27. 5 3.0 15.7 5.7 75. 7

x2=16. 2, p= .182

*p< .05; **p< .;01and***p< .001

73

)8 =29.7, p= .003"

Table 4-9
Travel lnfonnation Search and Travel Product Purchase Inﬂuenced by Age
(continued)

Travel Information Search Travel Product Purchase

 

 

.n h

x’ =26.5, p= .009"
'p < .05; “p < .01; and "*p< .001

74

1’ =10.5, p= .574

Age Online Ofﬂine 001%: n e None Online Ofﬂine 321m None
_ Car Rentals
06 1524 10.0% 5.0 17.5 67.5 4.9% 7.3 0 87.8
2534 24.9 4.8 8.0 62.2 17.1 9.6 2.8 70.5
3544 26.1 6.6 9.5 57.8 20.0 11.4 2.9 65.7
4554 27.1 3.6 7.8 61.5 21.4 9.9 2.6 66.1
55 or older 25.7 5.3 8.0 61.0 18.9 10.3 2.3 68.5
e V. x’=12..9.p=.378 , _.x’=11.4.p=.498
CA 18-24 17.1% 2.9 5.7 74.3 ’ 11.4% 17.1 0 71.4
2534 15.5 10.9 7.4 66.3 8.9 13.2 2.7 75.1
3544 15.1 5.6 7.5 71.8 8.8 11.7 2.6 76.9
4554 16.5 6.1 6.1 71.4 : 9.8 13.1 0.7 76.4
55 or older 15.9 7.0 6.6 70. 5 10. 9 13. 5 1.3 74. 3
, x’=9.2,p= .689 1 )1 =74, p= .830
Events
us 18-24 25.0% 10.0 17.5 47.5 4.9% 24.4 2.4 68.3
2534 ' 29.7 8.4 15.7 46.2 6.0 15.1 3.2 75.7
3544 32.2 5.2 23.3 39.4 8.3 14.3 3.7 73.7
4554 26.3 6.3 22.9 44.5 6.0 12.8 3.6 77.6
55 or older . 222. 3 6. 3 20. 0 51.3 6. 3 12. 3 3.6 77.8
, ... )1 =202 P= 064 w. 8 . . x2=77. P= .807
CA 1524 38. 9% 8. 3 11.1 41.7 x2. 9% 31.4 0 65.7
2534 314 8.9 22.1 37.6 4.7 21.9 2.3 71.1
3544 29.4 8.8 24.2 37.6 5.2 13.4 3.9 77.5
4554 28.7 7.4 19.3 44.6 5.4 17.5 2.0 75.1
550rolder 19.3 11.0 15.8 53.9 1.3 12.2 39 82.5
)1 ’=27. 9, p— 006“ x’=26.7, p: .009"
. Flights
us 18-24 - 27.5% 5.0 25.0 42.5 17.1% 22.0 2.4 58.5
2534 47.8 7.6 12.9 31.7 35.1 17.5 7.2 40.2
3544 44.8 2.3 13.2 39.7 33.7 13.1 4.3 48.9
4554 45.8 4.4 12.0 37.8 34.1 13.0 5.5 47.4
55 or older 39.7 5.7 15.7 39.0 32.8 18.9 4.0 44.4
.1 ’=22.9, p=_ .028' f» f x’=18.5,p=.101
CA 18-24 29.4% 5.9 29.4 35.3 16.7% 36.1 5.6 41.7
2534 36.4 10.5 18.6 34.5 . 16.4 32.0 5.1 46.5
3544 1 26.5 7.2 19.9 46.4 13.4 25.7 5.2 55.7
4554 29.4 6.8 17.9 45.9 14.8 27.3 5.1 52.9
55 or older 24.1 12.3 14.5 49.1 x 13.9 23.5 4.3 58.3

 

Age signiﬁcantly differed in regard to purchase behaviors on activities in the P
U.S., and activities, attractions and events in Canada (Table 4-9). US
respondents who were younger were more likely to purchase activities ofﬂine.
Canada respondents who were younger were more likely to purchase activities
ofﬂine. Younger Canada respondents were more likely to purchase attractions
and events ofﬂine than other age groups.

Education signiﬁcantly differed in regard to information search for activities,
car rentals and ﬂights in the U.S., and activities, attractions, car rentals, events
and ﬂights in Canada (Table 4-10). US respondents with higher education
(college degrees or more) were more likely to search for information on activities,
car rentals and ﬂights than US respondents with lower education (less than a
college degree). US respondents with higher education were likely to search for
information online on ﬂights than US respondents with lower education. Canada
respondents with higher education were more likely to search for information on
activities, events and ﬂights than Canada respondents with lower education.
Canada respondents with lower education were likely to search for information
online on attractions. Canada respondents with higher education were likely to
search for information on/ofﬂine on attractions.

In regard to purchase, education signiﬁcantly differed on attractions, car
rentals and ﬂights in the U.S., and activities and car rentals in Canada (Table 4-
10). US respondents with lower education were more likely to purchase
attractions than respondents with higher education. US respondents with higher

education were more likely to purchase car rentals and ﬂights online than US

75

Table 4-10

Travel Information Search and Travel Product Purchase Inﬂuenced by

 

 

Education
: Travel lnforrnation Search Travel Product Purchase
Education 7 On/ On/
E Online Ofﬂine ofﬂine None ? Online Ofﬂine ofﬂine N/A
Accommodations .
US Less than 3 ~ 48.5% 6.7 29.0 15.8 32.9% 26.8 10.2 30.1
college degree
College 2 54.0 5.2 27.8 13.0 34.7 30.2 8.9 26.3
degree or 2
more 3
V . V V _. . X2=521p=158 V .._X2=.3-34P= '281
CA Less than a } 52.0% 8.0 25.9 14.2 . 21.0% 39.8 7.4 31.9
college degree i
College 49.1 7.9 32.6 10.4 1 21.8 39.5 10.7 28.0
degree or
more : ?
; x’ =7.4, p= .059 x’ =4.4, p= .222
4 Activities
US Less than a i 21.4% 6.4 19.1 53.1 4.4% 15.6 3.9 76.2
college degree ;
College 25.6 9.0 21.9 43.6 5.2 18.4 3.7 72.8
degree or
more ;
_ V. x’=125p=005 ,x’=.2.4.p=...485
CA Less than a . 23.1% 7.7 18.7 50.5 2.2% 13.8 2.7 81.3
college degree
College 26.6 10.4 23.7 39.3 3.5 20.1 3.3 73.1
degree or
more i
_‘ £5,155.95 9041* ‘ x’ 558.15: 020*
; Attractions
US Less than a ; 32.9% 9.0 32.9 25.1 6.3% 21.7 5.1 66.9
college degree
College 30.7 10.3 32.7 26.2 3.7 19.3 4.0 73.0
degree or E
more &
, élx’_=1,.2..p.=...751 ._ . _ . .x’=8.1.p= 944*
CA Less than a 3 38.6% 8.5 28.4 24.6 3.2% 23.0 3.7 70.1
college degree f
College § 31.3 11.0 37.4 20.2 2.5 24.4 3.2 69.9
degree or .
more .
. 15:14.1, p= .003" x’=0.9, p: .822

 

* p < .05; “ p < .01; and “* p < .001

76

Table 4-10
Travel Information Search and Travel Product Purchase Inﬂuenced by
Education (continued)

Travel Information Search

Travel Product Purchase

 

Education On] On!
Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine N/A
, Car Rentals
US Less thana 22.3% 3.2 9.4 65.1 ‘ 15.2% 8.8 3.7 72.3
college degree
College 28.1 6.5 8.1 57.4 22.1 11.4 1.7 64.8
degree or
more
., V , x’=15.0,p= .002** x2=17.7,p= .001"
CA Less thana ~ 16.0% 4.5 5.7 73.8 11.1% 9.6 1.0 78.3
college degree : '
College 3 15.7 8.7 7.5 68.1 8.8 14.7 2.1 74.4
degree or .
more {
x’ =8.8, p= 033* x’ =9.1, p= .028'
’ Events
US Less thana ‘ 27.1% 5.5 23.4 44.1 8.1% 14.2 4.2 73.6
college degree
College 27.9 7.3 18.9 45.8 5.6 13.6 3.0 77.8
degree or
more ;
w _ w w x’.54.9.p= .176 . x’=5.1.p=.162
CA Less than a N 27.4% 8.5 15.2 48.9 5.9% 15.8 3.0 75.4
college degree ?
College ' 28.3 9.1 23.1 39.5 3.3 17.4 2.8 76.5
degree or
more
.x’=13.5. p= 10045 . x’ =4.5. p= .213
. Flights
US Less thana 36.6% 4.4 14.7 44.2 26.8% 14.5 4.4 54.3
college degree ‘
College 49.5 5.0 13.0 32.5 38.4 16.3 5.6 39.8
degree or ;
more ‘1
A u w. .e .e .e x’:252.p.=+.000*** _. ,x’=29.4.p=,.,ooo"*
CA Less than a a 26.4% 6.2 19.2 48.3 13.5% 26.4 4.4 55.7
college degree
College i 30.7 10.3 17.9 41.2 15.3 28.1 5.0 51.5
degree or
more
* x’=9.6, p= .022* x2=1.9. p= .599

 

* p < .05; ** p < .01; and ”* p < .001

77

respondents with lower education. Canada respondents with higher education
were more likely to purchase activities than Canada respondents with lower
education. Canada respondents with higher education were likely to purchase
car rentals ofﬂine, and respondents with lower education were likely to purchase

car rentals online.

Travel Information Search and Travel Product Purchase Inﬂuenced by
Travel Experience

Number of vacation trips in recent 12 months signiﬁcantly differed in regard to
information search for accommodations, activities, attractions and car rentals in
the U.S., and only car rentals in Canada (Table 4-11). US respondents who
vacationed only once were less likely to search for information online on
accommodations and car rentals compared to other respondents. US
respondents who vacationed four times were more likely to search for information
online on activities than others. Consistent patterns were not found in Canada.

Number of vacation trips in recent 12 months signiﬁcantly differed in regard to
the purchase of accommodations, car rentals and ﬂights in the U.S., and
activities and events in Canada (Table 4-11). US respondents who vacationed
more were more likely to purchase accommodations and car rentals online. US
respondents who vacationed ﬁve times or more were more likely to purchase
ﬂights online than others. Canada respondents who vacationed ﬁve times or

more were more likely to purchase events ofﬂine than others.

78

Table 4-11

Travel Information Search and Travel Product Purchase Inﬂuenced by

Number of Vacation Trips in Recent 12 Months
< Travel Information Search

Travel Product Purchase

 

Number of vacation '
trips In recent 12 V On! On/
months ‘ Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
; Accommodations
US 1 38.5% 9.3 31.9 20.3 27.9% 30.6 6.6 35.0
2 1 54.0 3.6 27.0 15.4 ‘ 32.4 34.1 7.6 25.9
3 50.7 4.6 31.7 13.0 32.2 30.8 7.7 29.4
4 ‘ 58.6 7.9 23.6 9.9 r 37.6 23.2 12.4 26.8
5 or more 53. 5 6.1 27.5 12. 8 38.5 23.4 12.6 25.5
f . )1 ’=29.6, p5 .003** f ‘ 5 x2=28.3, p5 .005**
CA 1 ‘ 50. 8% 7. 9 24.9 16.4 15.7% 40.8 6.3 37.2
2 5 51.5 8.8 27.9 11.8 20.2 41.3 9.4 29.0
3 ‘- 49.1 8.3 33.3 9.2 22.4 37.7 10.5 29.4
4 . 47.9 7.4 33.7 11.0 24.1 42.0 11.1 22.8
5 or more ‘- 50. 0 6. 9 32.2 10. 9 3 25.9 35.8 10.4 27. 9
; x ’=10.,0 p5 .615 x2=16.5,p= .168
Aetlvities
US 1 20.9% 9.9 20.3 48.9 4.9% 14.8 3.8 76.5
2 21.7 8.6 19.6 50.1 3.5 20.9 4.4 71.2
3 3 22.5 3.5 27.5 46.5 6.3 18.2 3.1 72.4
4 f 30.4 8.9 16.8 44.0 6.7 15.5 5.2 72.7
5 or more i 24. 8 9. 2 18.3 47.7 3. 7 14. 8 2.8 78.8
. .X 2524.0, p5 .020' .. - 12513.9 p= .310 7 5
CA 1 l 27. 9% 11. 1 15.8 45.3 x1.6% 21.5 1.6 75.4
2 3 27. 9 9.1 21.8 41.2 2.9 16. 6 3.5 77.0
3 ‘ 24.0 10.0 29.3 36.7 4.8 22.8 4.4 68.0
4 . 23.5 8.6 21.0 46.9 3.1 13.0 4.3 79.6
5 or more 21.3 8. 4 20.8 49. 5 2.5 14.9 1.0 81.6
‘ x2=182 p= 109 ______ ,. x2.=21-7.P?1042*,
. Attractions
US 1 i 30.8% 10.4 37.9 20.9 5.5% 21.3 3.3 69.9
2 5 34.7 9.5 31.2 24.6 4.4 23.8 5.3 66.5
3 1 27.1 11.3 37.7 23.9 5.2 20.6 3.8 70.3
4 V 39.3 9.9 27.2 23. 6 6.7 23.2 3.1 67.0
5 or more ‘ 228. 7 8. 3 30.6 32. 4 3. 4 14. 2 5.5 76. 9
x 2=23.,1 p= .027‘ f f 5 x2=18.,3 p= .108 5
CA 1 A 39. 5% 9. 5 32.6 18.4 x0. 5% 25. 0 4.2 70.3
2 39.1 10.3 29.1 21.5 2.3 25. 7 2.9 69.0
3 29.7 7.9 38.4 24.0 2.2 25.9 3.5 68.4
4 a 28.0 13.4 35. 4 23. 2 4.9 16.0 4. 9 74. 2
5 or more : 29. 7 10. 4 37. 6 22.3 5.0 23.4 2. 0 69.7
x.’=17.7 p= .124 x2=18.7,p= .097

 

p< .05; “p< .01; andx***p< .001

79

Table 4-11: Travel Information Search and Travel Product Purchase

Inﬂuenced by Number of Vacation Trips in Recent 12 Months (continued)

 

' Travel Information Search

Travel Product Purchase

 

Number of vacation 5
trips In recent 12 On/ On]
months 2 Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Car Rentals
US 1 17.6% 1.6 8.8 72.0 13.1% 8.2 1.6 77.0
2 28.5 5.0 9.2 57.3 17.4 14.4 2.9 65.3
3 ‘ 23.6 6.7 9.2 60.6 18.2 9.4 2.8 69.6
4 ; 24.6 2.6 8.9 63.9 19.1 5.2 2.1 73. 7
5 or more 29. 4 7. 0 7.3 56.3 ' 25.2 10. 8 2.8 61 .2
_,.x2=251P= 014' .. .x2=29-1P=-004“
CA 1 v 14.2% 6. 3 3.7 75.8 6. 3% 14.6 1.0 78.1
2 g 14.7 7.3 9.7 68.3 9.9 12.0 1.8 76.3
3 17.0 7.9 10.0 65.1 9.6 14.0 2.6 73.7
4 15.3 9.8 3.1 71.8 11.7 12.3 1.8 74.2
5 or more 17. 8 5. 0 5.0 72. 3 10.9 11.9 1.0 76.1
ix ’=21.2,p= 048* x2=7.0,p=.855
Events
US 1 1 22.0% 5.5 24.7 47.8 4.9% 16.4 2.2 76.5
2 27.9 5.9 20.5 45.7 5.6 14.4 3.2 76.8
3 ; 27.5 8.1 22.5 41.9 8.7 13.6 3.8 73.8
4 7 31.4 7.9 20. 9 39. 8 9.3 11.9 3.1 75. 8
5 or more . 218.1 5. 5 17. 4 48. 9 5.2 13.2 4.6 76. 9
:x ’512.,5 p= .405 .. x2=10.4,p= .578
CA 1 . 30. 5% 7. 4 16. 3 45.8 6.8% 13.1 1.6 78.5
2 30.2 7.6 18.8 43.4 3.2 17.0 2.9 76.8
3 24.5 10.0 28.4 37.1 3.1 16.7 6.1 74.1
4 , 23.2 12.8 18.9 45.1 2.5 13.0 2.5 82.1
5 or more f 2?. 4 8. 5 18. 4 43.8 6.0 22.9 1.0 70.1
x ’=20.,0 p5 .067 kkkkk x2=28.6,p= .005** 5
7 Flights
US 1 ; 35.2% 4.4 17.0 43.4 25.7% 18.0 3.8 52.5
2 5 43.0 4.7 11.9 40.4 ; 30.9 14.4 6.2 48.5
3 ' 42.3 5.3 15.5 37.0 35.0 16.4 4.5 44.1
4 ; 45.0 4.2 12. 6 38. 2 29.4 20.1 4.6 45. 9
Sormore V 50.8 4.9 12.8 31.5 41.2 11.7 5.2 41.8
i x. ’516.,2 p5 .181 f - x2=23.1,p= 027*
CA 1 A 21.7% 7. 4 22.8 48.1 8.3% 32.8 5.2 53.6
2 S 30.9 7.4 17.4 44.4 15.2 28.1 2.6 54.1
3 2 28.4 11.8 21 .0 28.9 » 18.0 25.9 7.0 49.1
4 a 32.7 8.6 15.4 43.2 , 14.2 26.5 7.4 51.9
5 or more V 31.3 9.0 14. 9 44.8 { 16.0 24.0 4. 0 56.0
* 1’15-95, p5 .197 ? x2=19.8,p= .072

 

* p < .05; *" p < .01; and *** p < .001

80

Travel Information Search and Travel Product Purchase Inﬂuenced by
General lntemet Use

Years of lntemet use signiﬁcantly differed in regard to information search for
ﬂights by US respondents, and accommodations, activities, attractions, events
and ﬂights by Canada respondents (Table 4-12). US respondents who used the
lntemet longer were likely to search for information online on ﬂights. Canada
respondents who used the lntemet less than two years were less likely to search
for information online on accommodations, activities, attractions, events and
ﬂights than those who used the lntemet for four to six years.

Years of lntemet use signiﬁcantly differed in regard to the purchase of ﬂights
in the U.S. and car rentals in Canada (Table 4-12). US respondents who used
the lntemet longer years were more likely to purchase ﬂights online. Speciﬁc
patterns were not found in Canada.

Time spent online per week did not signiﬁcantly differ in US respondents'
lnforrnation search. This element signiﬁcantly differed in regard to information
search for accommodations by Canada respondents (T able 4-13). Canada
respondents who spent 21 hours or more per week online were likely to search
for information online on accommodations.

Time spent online per week signiﬁcantly differed in purchasing events and
ﬂights in the U.S., however, it did not signiﬁcantly differ in Canada (Table 4-13).
US respondents who spent time online longer were more likely to purchase

events and ﬂights online.

81

Table 4-12

Travel Information Search and Travel Product Purchase Inﬂuenced by

Years of lntemet Use

 

Travel Information Search

Travel Product Purchase

 

Years of the On/ On/
lntemet use Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Accommodations
us ”’55 ":22; 42.9% 6.8 35.2 15.1 27.1% 32.1 6.3 34.4
24 years 51.6 5.7 29.7 13.0 35.0 26.5 11.9 26.5
45 years 53.5 5.0 25.7 15.7 33.5 29.3 8.9 28.3
M°'° “:2: 55.8 6.5 24.4 13.3 38.0 28.2 8.8 25.0
_ x2=13.5,p= .141 x2=16.1, p= .065
CA “’33 2:22; 45.9% 10.2 27.6 16.3 14.7% 45.7 6.6 33.0
24 years 50.3 7.4 32.8 9.5 23.0 40.1 9.0 28.0
4-6 years 55.7 8.6 23.9 11.8 23.7 28.8 9.0 28.5
M“ ‘32:; 46.2 6.0 36.3 11.5 22.6 34.0 13.6 29.8
.Hx’519..6.p=. 021*... 185155. p= .058
V Actlvlties
us Less ‘32:; 18.3% 9.1 19.2 53.4 4.5% 17.2 1.8 76.5
24 years ' 24.1 8.4 18.4 49.1 5.6 16.1 3.9 74.5
4-6 years 2 24.4 7.9 22.3 45.4 4.2 17.3 4.7 73.8
”"9 ":22: 26.3 6.5 22.4 44.8 4.9 18.8 3.6 72.7
x2 =9.8, p5 .365 x2 55.1, p5 .821
CA Less ":22: 23.6% 8.7 24.6 43.1 2.0% 15.7 1.5 80.8
24 years 21.4 10.3 20.9 47.4 3.2 17.7 2.9 76.3
4-6 years 29.0 10.2 17.2 43.6 3.5 17.6 3.2 75.7
M” ":2: 28.2 7.3 27.8 36.8 3.0 20.0 4.7 72.3
_ x’=18.1.p5.034* x2 55.7.15= .672
V Attractions
us ”’55 23:2; . 28.3% 10.0 35.2 26.5 4.5% 22.6 4.5 68.3
24 years . 30.0 10.8 33.7 25.6 5.6 20.0 4.1 70.3
45 years 32.0 8.9 32.8 26.2 5.0 19.1 4.5 71.5
M°'° “:2: 36.0 9.4 29.2 25.3 3.9 21.1 4.2 70.8
, __ _ {555.155.7113 . , _ .._.x2=2_-4.P=. .985
CA “’53 ":2; 31.8% 11.3 36.4 20.5 2.0% 25.9 3.0 69.0
24 years 33.6 13.5 33.6 19.3 2.6 24.5 2.6 70.2
4-6 years 37.6 7.3 29.6 25.5 4.2 23.6 2.2 70.0
M°'° 2322: 31.3 7.3 39.9 21.5 2.1 20.9 6.8 70.1
38518.4, p5 031* x’5140, p= .124

 

*p< .05; **p< .01; and ***p< .001

82

Table 4-12

Travel Information Search and Travel Product Purchase Inﬂuenced by

Years of Internet Use (continued)

 

Travel Information Search

Travel Product Purchase

 

Years of the On/ Onl
lntemet use Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
3 Car Rentals
us ”’55 2:22; 19.2% 6.4 5.5 68.9 14.9% 10.4 0.9 73.8
24 years 24.8 4.9 10.3 60.0 19.2 10.0 3.6 67.2
4-6 years 28.9 4.5 8.9 57.7 20.4 10.5 3.4 65.7
Mm 2322: 27.3 5.2 8.4 59.1 20.8 10.1 1.3 67.9
. . .x2=13-7. 125.134 .x’=11.5.p= .244
CA “’53 "3:2; 19.5% 4.6 5.1 70.8 9.7% 11.7 0 78.6
24 years 15.1 7.9 7.1 69.8 9.7 15.0 1.1 74.2
4-6 years . 14.6 8.9 8.6 67.8 9.9 14.1 2.2 73.7
Mme 2:22: 15.4 5.6 6.0 73.1 8.5 . 8.1 3.8 79.5
x2 =94, p= .400 ,. x2 517.9, p= 037*
Events
us ”’53 2:22: 24.2% 3.7 22.4 49.8 4.1% 15.8 3.2 76.9
24 years 25.3 8.4 20.9 45.5 6.6 11.9 3.4 78.1
4-6 years 27.0 6.8 21.8 44.4 6.0 12.6 4.2 77.2
M°'° 22:2: ' 33.8 5.5 17.9 42.9 9.1 16.6 2.6 71.8
x2 =14.6, p= .103 x2 511.4, p= .247
CA Less ":2; 20.0% 8.2 23.6 48.2 2.5% 13.2 1.5 82.7
24 years . 23.8 10.8 19.3 46.0 5.0 17.1 2.1 75.8
4-6 years 34.7 8.0 18.2 39.2 4.8 18.5 3.2 73.5
Mme ‘3:ng 32.3 8.1 20.9 38.7 3.9 16.3 5.2 74.7
165217. p=_-010* x’=12.o. 155.215
. Flights
us “’33 23:2: , 32.0% 5.9 14.6 47.5 24.9% 15.8 3.2 56.1
24 years . 40.3 4.7 16.5 38.6 32.8 16.5 5.1 45.5
4-6 years 48.6 5.5 13.4 32.5 34.8 16.8 7.1 41.4
M°'° 2322: 52.3 3.2 9.7 34.7 38.6 12.3 3.9 45.1
x . ._ x2 512.5. p= .005** ‘_ x2 =22.5. p= .008"
CA “’33 2322': 1. 21 .4% 7.1 21.9 49.5 8.2% 28.6 3.1 60.2
24 years 27.0 8.2 19.6 45.2 15.0 28.9 4.7 51.3
4-6 years 34.4 10.5 12.4 42.7 15.3 27.5 5.8 51.4
M°'° 2:22: 32.2 8.6 20.6 38.6 18.7 23.4 6.0 51.9
x2 521.7, p5 010* x2=14.8, p= .095

 

* p < .05; ** p < .01; and p < .001

83

Table 4-13
Travel Information Search and Travel Product Purchase Inﬂuenced by Time

 

 

Spent Online Per Week
Travel Information Search ‘ Travel Product Purchase
Time spent online ; On/ On/
per week Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Accommodations

us 4 hours or less 1 55.2% 5.2 25.0 14.6 ' 32.5% 30.2 10.3 27.0
510 hours i 50.8 7.3 28.8 13.0 29.8 32.6 9.8 27.8
11-20 hours 49.8 3.1 31.7 15.4 38.9 26.6 7.5 27.0
21 hours or
more 3 51.2 7.5 27.2 14.1 35.1 25.0 9.8 30.1
5 x2 511.5, p= .246 H ,, x2=11-0. p= .277
CA 4 hours or less 48.3% 7.3 33.7 10.8 . 19.1% 39.9 11.4 29.6
510 hours i 48.7 7.7 32.6 11.0 ' 22.3 38.5 8.7 30.4
11-20 hours 44.7 9.7 30.4 15.2 24.4 41.2 7.6 26.9
21 202112123; 1 60.2 7.4 21.2 11.3 g 21.4 38.5 9.8 30.3
; x2 =18.7, p= 028* * x2 55.5, p= .793
. Activities
US 4 hours or less 27.9% 8.4 20.1 43.5 _ 6.8% 18.0 3.2 72.0
510 hours ; 23.7 10.2 20.3 45.8 r 2.8 19.4 2.2 75.6
11-20 hours ; 21.6 6.3 21.0 51.1 4.4 14.7 4.7 76.2

2109939' E 21.9 6.6 21.0 50.6 ; 5.7 16.7 4.8 72.9

more :
, . h . wx’=1o.5.p-—,-. 1315 . 5125.125 .157 V,
CA 4 hours or less ; 24.8% 10.2 23.6 41.4 2.6% 22.2 2.9 72.2
510 hours ; 25.5 9.4 22.6 42.6 4.2 17.4 4.2 74.2
11-20 hours ' 26.5 8.4 20.6 44.5 i 1.7 13.0 3.8 81.5
21 hours or
more . 24.8 9.1 20.4 45.7 3.0 16.3 1.3 79.4
x2=2-.3. p5 .985 . f x2=16.3, p5 .061
Attractions
us 4 hours or less 31.8% 11.7 32.1 24.4 4.5% 18.6 3.2 73.6
510 hours 2 32.2 11.0 32.2 24.6 3.9 23.3 3.7 69.1
11-20 hours 1 29.2 10.0 32.9 27.9 3.8 21.9 3.4 70.8
21 hours or 3 '
more 33.5 6.6 33.2 26.6 7.1 17.6 6.8 68.5
x’=.7_.4.p= .595 . V, .x’=16.3.p= 061
CA 4 hours or less 3 34.0% 11.9 35.8 18.3 3.2% 28.0 3.2 65.6
510 hours * 32.7 9.1 34.0 24.3 7 1.6 19.8 3.2 75.3
11-20 hours 37.6 9.7 28.7 24.1 4.2 22.2 5.0 68.6
2‘ ““333; g 32.3 9.5 37.5 20.7 ‘ 2.6 24.4 2.1 70.9
i x’ 59.4, p= .404 x2=13.6, p5 .139
* p < .05; ** p < .01; and m p < .001

X

Table 4-13
Travel lnforrnation Search and Travel Product Purchase Inﬂuenced by Time
Spent Online Per Week (continued)

 

 

Travel Infonnatlon Search Travel Product Purchase
Time spent online On/ On/
per week Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Car Rentals

us 4 hours or less ‘ 25.0% 6.5 6.8 61.7 17.4% 12.9 1.9 67.8
510 hours i 27.1 5.1 8.5 59.3 19.4 12.4 1.7 66.6
11-20 hours 25.1 3.1 7.8 63.9 1 16.9 7.8 2.8 72.4
21 hours or
more 25.1 5.7 11.4 57.8 22.9 7.7 3.9 65.5
x2 59.7, p= .372 x2 =16.5, p= .056
CA 4hours or less 15.7% 8.1 9.0 67.2 9.6% 14.9 2.0 73.4
510 hours 13.2 8.1 7.4 71.3 11.3 13.9 2.3 72.5
11-20 hours 17.3 5.9 6.3 70.5 9.2 13.0 0.4 77.3
21 hours or _
more 17.8 6.1 3.5 72.6 7.7 8.1 2.1 82.1
. x2=11.0,p= .275 N 185125, p= .184
' Events
us 4hours or less 30.8% 6.2 16.6 46.4 5.1% 15.1 1.9 77.8
510 hours ‘ 28.2 7.9 20.3 43.5 4.5 13.5 3.1 78.9
11-20 hours 25.1 6.6 20.7 47.6 6.9 15.7 3.1 74.3

21”“ °' ‘ 26.3 5.1 24.9 43.7 9.8 11.3 5.4 73.5

more
N .e a .. ‘ ,‘.;_x’-_-1o-5.p=‘.305 , , ’=15.3. 155 932*
CA 4 hours or less ‘ 21.8 8.1 22.1 48.0 2.3 19.6 1.8 76.3
510 hours ; 31.3 10.6 17.1 41.0 5.2 16.1 2.9 75.8
11-20 hours 3 28.2 9.7 20.2 42.0 5.0 17.6 3.8 76.5

21 hours or
more 32.6 7.0 21.3 39.1 5.2 12.4 3.9 78.5
x2 =15.4, p5 .080 A x2=12-0. p5 .213
Flights
us 4 hours or less ' 44.2% 4.9 13.6 37.3 31.5% 18.3 6.4 43.7
510 hours 42.7 5.6 13.3 38.4 31.2 19.7 3.7 45.5
11-20 hours 42.9 4.4 11.9 40.8 34.5 12.5 3.8 49.2
2‘ “Ugo: 46.7 4.2 15.9 33.2 36.6 11.3 6.5 45.5
: x’=6.2, p= .717 f f . 78519.4, p5 022*
CA 4 hours or less 28.3% 9.3 19.2 43.1 13.5% 28.9 4.7 52.9
510 hours 28.7 10.0 18.1 43.2 16.2 26.9 5.8 51.0
11-20 hours 26.5 7.6 19.7 46.2 13.4 27.2 4.2 55.2
21 “use: 33.9 7.8 15.2 43.0 15.5 25.8 4.7 54.1
r x’ =5.8, p= .759 x’ 53.2, p5 .958
* p < .05; ** p < .01; and “* p < .001

X

85

Speed of lntemet connection signiﬁcantly differed in regard to information
search for accommodations, activities, car rentals and ﬂights in the U.S., and
activities and car rentals in Canada (Table 4-14). US respondents with high
speed were more likely to search for information online on accommodations,
activities, car rentals and ﬂights than US respondents with regular speed.
Canada respondents with high speed were more likely to search for information
online on activities and car rentals than Canada respondents with regular speed.
The gap of online usage in Canada for information search between high speed
users and regular speed users was smaller than the gap in the U.S.

Speed of the lntemet connection signiﬁcantly differed in purchasing
accommodations, activities, car rentals and ﬂights in the U.S. lntemet speed did
not signiﬁcantly differ in Canada (Table 4-14). US respondents with high speed
were more likely to purchase accommodations, car rentals and ﬂights online than

US respondents with regular speed.

86

Table 4-14

Travel Information Search and Travel Product Purchase Inﬂuenced by
Speed of lntemet Connection

 

Travel Information Search

Travel Product Purchase

 

Speed of the On/ On!
lntemet connection Online Ofﬂine ofﬂine None Online Ofﬂine offline None
V Accommodations
us High speed 57.1% 6.1 22.8 14.0 38.6% 29.4 8.1 23.9
Regular speed 48.0 5.7 32.0 14.2 30.8 28.3 10.3 30.7
x2 514.7, p5 .002" x2 512.7, p5 .005**
CA High speed 50.3% 8.1 29.4 12.2 22.2% 39.6 9.2 28.9
Regular speed 49.9 7.8 31.0 11.2 20.7 39.5 9.9 30.0
x2 =0.5, p= .911 x2 =0.6, p= .907
, Activities
us High speed 26.8% 7.2 22.8 43.3 5.9% 19.7 4.2 70.3
Regular speed 21.8 8.3 19.2 50.7 4.0 15.5 3.5 77.0
x2 59.4, p= 024* _ x2 57.9, p5 049*
CA High speed 25.8% 10.1 24.5 39.7 3.4% 18.8 3.0 74.9
Regular speed 24.8 8.8 19.0 47.4 2.5 16.9 3.0 77.6
x2 =8.2, p= 042* x2 51.5, p5 .675
Attractions
us High speed 32.1% 10.2 30.2 27.5 3.8% 21.4 4.5 70.3
Regular speed ‘ 31.5 9.5 34.4 24.6 5.5 19.6 4.3 70.6
f x’530, p5 .386 w * x2=2-5. p= .476 N V
CA High speed , 32.5% 9.7 36.9 20.9 3.2% 21.8 3.5 71.5
Regular speed ,- 35.6 10.5 31.0 22.9 g 2.3 26.2 3.2 68.3
: x2 =4.2, p= .242 x2 3.6, p= .302
. Car Rentals
us High speed ' 29.6% 5.1 6.8 58.4 1 23.1% 10.0 2.5 64.4
Regular speed ; 23.0 5.0 9.8 62.2 16.5 10.4 2.6 70.5
f 7859.5, p= 023* f . x2 59.0, p= 029*
CA High speed 16.8% 8.7 5.0 69.5 10.7% 12.9 1.8 74.6
Regular speed . 14.7 5.5 9.0 70.9 . 8.3 12.9 1.7 77.0
x2 511.0, p= 011* x2 51.9, p= .601
Events
us High speed 29.6% 8.0 18.8 43.6 6.8% 14.8 3.6 74.8
Regular speed A 26.1 5.5 22.2 46.2 6.4 13.3 3.5 76.8
_ . ; 7856.5, p= .091 7850.8, p= .851
CA High speed 27.1% 9.5 22.6 40.8 4.7% 17.8 2.7 74.9
Regular speed : 28.8 8.4 17.5 45.3 3.8 15.7 3.2 77.2
. x2 =5.6, p= .135 {51.7, p= .641
Flights
us High speed 51.8% 4.6 10.8 32.8 39.6% 16.1 4.9 39.4
Regular speed 38.8 4.9 15.6 40.6 29.3 15.1 5.1 50.4
x2 522.6, p5 .000*** x2 518.7, p= .000***
CA High speed 31.8% 8.7 19.1 40.5 16.8% 27.8 4.9 50.6
Regular speed 26.1 9.0 17.4 47.5 12.3 26.9 4.9 55.8
12:67, p= .083 x’=5.2, p= .156

 

* p < .05; *‘ p < .01; and m p < .001

87

Credit card use for online purchase signiﬁcantly differed in regard to
information search for accommodations and ﬂights in the U.S., and car rentals
and ﬂights in Canada (Table 4-15). US respondents with credit card use for
online purchasing were likely to search for information online on
accommodations and ﬂights. US respondents who did not use credit cards for
online purchase were likely to search for information onlofﬂine on
accommodations and ﬂights. Canada respondents with credit card use were
likely to search for information online on car rentals and ﬂights.

Credit card use for online purchase signiﬁcantly differed in regard to the
purchase of accommodations, car rentals, events and ﬂights in the U.S., and
accommodations, activities, attractions, car rentals, events and ﬂights in Canada
(Table 4-15). US respondents with credit card use for online purchase were
likely to purchase accommodations, car rentals and ﬂights online. US
respondents who did not use credit cards for online purchase were likely to
purchase accommodations and ﬂights ofﬂine. Canada respondents with credit
card use were likely to purchase accommodations, car rentals and ﬂights online.
Canada respondents who did not use credit cards were likely to purchase

accommodations, attractions, events and ﬂights ofﬂine.

88

Table 4-15

Travel lnfonnation Search and Travel Product Purchase inﬂuenced by
Credit Card Use for Online Purchase

 

 

Travel lnforrnation Search Travel Product Purchase
Credit card 2
Usage for :
online V On/ On/
purchase a Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
3 Accommodations
us Yes ; 53.2% 5.2 27.4 14.2 36.8% 26.3 9.8 27.1
No ; 39.9 10.8 35.8 13.5 11.5 48.0 6.1 34.5
H .x’=15.0.p= .002" ,V x’=5o.9.p=.ooom
CA Yes 1 52.4% 7.1 29.4 11.2 27.7% 34.0 10.9 27.4
No ‘ 44.5 10.1 32.2 13.2 6.0 53.5 6.0 34.6
‘ x2=6.9, p5 .077 x2=81.7, p5 .000***
‘ Activities
us Yes : 24.3% 7.7 20.7 47.3 4.9% 16.9 3.7 74.5
No ; 19.6 9.5 20.3 50.7 A 4.1 19.6 4.1 72.3
, .1252-14P=-555V .. +,._ w , x’=o.9.p=.827. M ,
CA Yes ; 25.9% 9.5 20.8 43.7 3.8% 16.5 3.7 76.0
No i 24.0 9.1 24.6 42.3 0.6 21.5 1.3 76.6
, x2 =20, p5 .578 x2 515.7, p5 .001**
1 Attractions
us Yes j 32.2% 9.8 31.5 26.4 5.1% 19.4 4.3 71.2
No 27.7 9.5 42.6 20.3 1 2.7 27.7 5.4 64.2
,V ix’=‘_7e.7.p= .053 e x2=7-3. 125.054 _
CA Yes . 32.4% 10.2 33.6 23.8 3.7% 20.7 3.7 71.9
No ? 37.9 9.8 35.6 16.7 0.3 31.9 2.5 62.3
. (X2 57.6. p= .056 x2=23.8,p=.000"*
g Car Rentals
us Yes ; 26.3% 4.7 8.4 60.5 20.8% 9.9 2.2 67.0
No 19.6 8.1 10.1 62.2 5.4 12.8 5.4 76.4
  112 559.5115 ‘ ‘ x’=24.4.p= .000***
CA Yes . 17.7% 7.7 7.1 67.5 11.8% 11.5 2.1 74.6
No x 10.7 6.0 6.6 76.7 4.1 16.1 0.6 79.1
_ y. x2 510.7. 115.015.. _ _. , x’=21.1._p= .000***
1 Events
us Yes , 28.2% 6.7 20.3 44.8 7.4% 13.2 3.6 75.8
No ; 22.3 4.7 25.0 48.0 0.7 18.9 3.4 77.0
V V ' 1851.2. 155242 . x’=12.o. p=_ 4107"
CA Yes ; 29.0% 8.9 19.8 42.3 5.3% 14.6 3.6 76.5
No 3 25.3 8.9 21.5 44.3 1.6 22.4 1.3 74.8
N. 118515.. 125.550 _ x2 520.0. 125.000“
. Flights
us Yes 2 46.1% 4.6 12.9 36.4 36.6% 13.7 5.1 44.6
No ; 27.0 6.1 20.3 46.6 7.4 29.7 4.7 58.1
. ; x2=20.4. P= 000'”. , ., . . x2=60-5. P= 000*"
CA Yes 1 32.3% 9.3 18.1 40.3 18.2% 25.5 6.3 50.0
No i 21.2 7.6 18.7 52.5 5.7 32.4 1.3 60.7

x2 =18.2, p= .000***

* p < .05; ** p < .01; and p < .001

89

x2 545.3, p= .000***

Travel Information Search and Travel Product Purchase Inﬂuenced by
Internet Use for the Latest Trip

Planning horizon for lntemet use for the latest tn’p signiﬁcantly differed in
regard to information search for activities, attractions, car rentals and ﬂights in
the U.S., and accommodations, activities, attractions, car rentals and ﬂights in
Canada (Table 4-16). US respondents who used the Internet for a longer period
for the latest trip were more likely to search for information onlofﬂine for activities,
attractions and ﬂights. Canada respondents who used the lntemet for a longer
period for the latest trip were more likely to search for information on/ofﬂine for
accommodations, activities, attractions and ﬂights, and online for car rentals.

Planning horizon for lntemet use for the latest tn'p signiﬁcantly differed in
purchasing accommodations, activities, car rentals and ﬂights in the U.S., and
accommodations, car rentals and ﬂights in Canada (Table 4-16). US
respondents who used the lntemet for a longer period for the latest trip were
more likely to purchase accommodations, activities and ﬂights. US respondents
who used the Internet for four months or more for the latest trip were more likely
to purchase accommodations, activities, car rentals and ﬂights ofﬂine than
others. Canada respondent who used the Internet for a longer period for the
latest trip were more likely to purchase accommodations onlofﬂine, and car

rentals and ﬂights ofﬂine.

90

Table 4-16

Travel lnfonnation Search and Travel Product Purchase Inﬂuenced by
Planning Horizon for Internet Use for the Latest Trip

Travel Information Search

Planning horizon for

Travel Product Purchase

 

the lntemet use for On] On/
the latest trip Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
. Accommodations
us ”’33 33k: 50.6% 7.0 24.4 18.0 30.3% 25.1 4.6 40.0
24 weeks 53.7 5.9 22.4 18.0 32.9 26.7 7.8 32.5
1-2 months 52.3 5.0 27.8 14.9 38.1 23.5 11.0 27.4
24 months 48.5 5.7 32.8 13.0 34.1 33.1 8.4 24.4
4 220221526125 ’ 53.5 6.0 31.8 8.7 32.0 33.7 13.5 20.8
. _ x2 =20.6, p5 .057 . x2 540.3, p= .000***
CA “’53 $32k: ; 55.4% 4.8 20.2 19.6 23.7% 36.7 1.8 37.9
24 weeks 50.9 12.4 24.3 12.4 16.4 43.8 7.1 32.7
1-2 months * 48.3 6.3 32.4 13.0 22.0 38.1 10.6 29.2
24 months 46.5 9.2 33.6 10.6 19.3 38.1 12.8 29.8
4 ”"23: _ 50.6 6.3 36.8 6.3 26.0 40.5 13.0 20.4
ij’5140, p= .000*** . x2 =38.5, [F .000***
. Activities
us ”’53 3:3; ‘ 18.6% 8.1 12.8 60.5 2.3% 10.3 1.1 86.3
24 weeks 3 23.9 5.9 14.9 55.3 1.6 17.3 4.7 76.5
1-2 months E 23.8 8.5 20.6 47.0 4.6 15.3 3.6 76.5
24 months 25.4 7.7 23.4 43.5 7.4 16.1 3.3 73.2
2‘ manso‘r: ‘ 25.4 9.4 27.4 37.8 6.9 23.8 5.0 64.4
i 12 =38.2, p= 000*** x2 =40.8, p5 .000***
CA “'2’” wk: . 23.8% 11.3 16.7 48.2 2.4% 17.3 1.2 79.2
24 weeks » 23.6 8.0 14.2 54.2 0.9 17.7 2.7 78.8
1-2 months ' 27.0 9.7 21.1 42.2 2.1 18.7 4.3 74.9
24 months 27.9 11.0 23.7 37.4 3.7 17.9 2.3 76.1
4 ””22: i 24.2 8.2 30.9 36.8 5.2 18.1 4.1 72.7

x2 533.5, p= .001**

‘p< .05; **p< .01; and ""p< .001

91

x2 514.4, p5 .279

Table 446
Travel Information Search and Travel Product Purchase Inﬂuenced by
Planning Horizon for Internet Use for the Latest Trip (continued)

 

 

V Travel Information Search , Travel Product Purchase
Planning horizon for ' L
the lntemet use for On/ On/
the latest trip ? Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
’ Attractions
us Less "‘3" 2 a 21.5% 8.7 26.2 43.6 . 5.1% 14.3 1.7 78.9

weeks :
2-4 weeks V 37.6 9.4 25.9 27.1 j 3.5 21.2 5.5 69.8
1-2 months ; 32.7 9.3 30.6 27.4 2 5.3 21.4 2.8 70.5
2-4 months ' 29.4 9.7 34.8 26.1 - 4.7 18.7 4.7 71.9

4m°""‘s°"334.1 11.0 42.8 12.0 7 5.6 23.1 6.6 64.7

more ,
.. .x2=70.1.p=,000*‘* , {=18.0,p= .117
Less than2 :
CA weeks - 33.3% 10.1 22.0 34.5 1.8% 20.8 3.0 74.4

2-4 weeks 36.4 9.8 30.7 23.1 . 2.7 24.8 4.9 67.7
1-2 months i 33.3 9.3 36.7 20.7 ' 1.7 25.0 3.4 69.9
2-4 months 2 32.7 14.3 35.5 17.5 2.8 21.1 2.3 73.9

4mm“ °' 34.0 7.8 41.0 17.2 g 4.8 26.2 3.3 65.7

more 3
, '.wx’=355.p{=,ooo*** 4{x’=11.7.p=.47o
; Car Rentals
us “’33 than2 18.6% 1.7 6.4 73.3 13.1% 4.0 0.6 82.3

weeks
24 weeks ' 18.0 3.5 7.1 71.4 14.5 7.8 4.7 72.9
1-2 months ' 29.9 5.0 11.0 54.1 V 26.3 10.7 1.1 61.9
2-4 months . 24.4 7.7 8.4 59.5 3 17.7 10.7 2.0 69.6

4'09""‘5 9' 33.1 6.0 9.4 51.5 21.5 15.2 3.6 59.7

more .
* { =46.5, p5 .000*** j { 552.7, p5 .000***
Less than2 . V 0
CA weeks,10.7% 5.9 3.6 79.9 : 7.1/o 8.3 1.2 83.4

24 weeks 12.8 7.5 5.8 73.9 ‘ 8.0 8.0 1.8 82.2
1-2 months 1 14.3 6.3 6.7 72.7 } 9.3 11.4 2.5 76.7
24 months 18.9 6.9 6.0 68.2 ; 12.3 13.7 0.5 73.5
4 ””222: ’ 20.4 9.3 10.8 59.5 11.1 20.7 2.6 65.6

’ {528.5, p= .005** 9 {534.9, p5 .000***

 

* p < .05; ** p < .01; and p < .001

92

Table 4-16
Travel Information Search and Travel Product Purchase Inﬂuenced by
Planning Horizon for Internet Use for the Latest Trip (continued)

; Travel Information Search
Planning horizon for _

Travel Product Purchase

 

the lntemet use for On/ ; On/
the latest tip ' Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Events
us Less 3,“:ng 18.0% 5.8 20.3 55.8 5.7% 10.3 1.7 82.3
24 weeks 26.7 8.2 16.5 48.6 6.3 14.9 3.1 75.7
1-2 months 29.9 5.7 19.9 44.5 5.7 13.9 4.6 75.8
24 months i 29.4 6.7 21.4 42.5 7.7 15.4 3.3 73.9
4 ”may; 29.4 5.7 24.4 40.5 7.3 14.2 4.3 74.3
. : { 520.3, p= .062 , { =7.8, p5 .798
CA Less $33k: 29.8% 11.3 13.7 45.2 i 5.9% 17.8 1.8 74.6
24 weeks 2 28.3 8.4 21.2 42.0 4.4 15.5 1.8 78.3
1-2 months 31.1 7.6 21.4 39.9 4.2 15.2 4.2 76.4
24 months ‘ 26.1 9.2 20.2 44.5 3.7 14.2 2.8 79.4
4 ”"222: I 25.1 9.0 22.1 43.8 . 4.1 20.7 3.7 71.6
.12 590.155 17.06, . x’=1o.1.p= .604
Flights
105 “’35 $3; ; 37.8% 1.7 7.6 52.9 g 25.7% 4.0 2.3 68.0
24 weeks i 39.6 3.5 9.8 47.1 33.3 6.7 5.1 54.9
1-2 months . 46.6 3.2 13.5 36.7 1 34.9 16.4 3.9 44.8
24 months ‘ 47.2 5.0 16.7 31.1 g 38.5 14.7 5.4 41.5
4 ”may; f 46.8 8.4 17.7 27.1 ' 32.0 29.4 7.6 31.0
V { 560.4, p= .000*** { 5117.8, p= .000***
CA “’33 $32k: 24.2% 2.4 14.8 58.6 13.7% 12.5 1.8 72.0
24 weeks 30.2 6.7 12.9 50.2 3 15.9 18.6 4.9 60.6
1-2 months ; 31.1 10.9 16.0 42.0 ' 14.5 27.7 4.3 53.6
24 months 30.9 13.4 18.4 37.3 , 15.6 34.9 4.6 45.0
4 ”"2223; 27.9 9.3 27.5 35.3 14.0 38.7 7.7 39.5

3 {552.7, p= .000***
* p < .05; ** p < .01; and *** p < .001

93

’ { 571.5, p= .000***

 

Time spent online for the latest tn'p signiﬁcantly differed in regard to L
information search for all six products in the U.S. and Canada (Table 4-17).
The more time US respondents spent online for the latest trip, the more they
were likely to search for information online and onlofﬂine on all six products, and
the more they were likely to search for information ofﬂine on car rentals and
ﬂights. However, they were less likely to search for information ofﬂine on
accommodations and activities. The more time Canada respondents spent
online for the latest trip, the more they were likely to search for information online
on activities, attractions, car rentals and ﬂights, and the more they were likely to
search information ofﬂine on ﬂights. However, they were less likely to search for
information ofﬂine on activities, attraction and events.

Time spent online for the latest tn’p signiﬁcantly differed in purchasing all six
products in the U.S., and accommodations, activities, car rentals and ﬂights in
Canada (Table 4-17). The more time US respondents spent online for the latest
trip, the more they were likely to purchase accommodations, activities,
attractions, car rentals and events online and onlofﬂine, and ﬂights onlofﬂine. In
addition, they were more likely to purchase activities, attractions, car rentals and
ﬂights ofﬂine. The more time Canada respondents spent online for the latest trip,
the more they were likely to purchase accommodations, activities and car rentals
online and on/ofﬂine, the more they were likely to purchase attractions, events
and ﬂights onlofﬂine, and the more they were likely to purchase car rentals and
ﬂights ofﬂine. However, they were less likely to purchase accommodations

ofﬂine.

94

Table 4-17

Travel Information Search and Travel Product Purchase Inﬂuenced by Time
Spent Online for the Latest Trip

 

Travel Information Search

Travel Product Purchase

 

Time spent online On] On/
for the latest trip Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Accommodations
us 2 hours or less , 45.9% 6.8 25.8 21.5 1 29.8% 27.6 5.9 36.7
3-5 hours 55.0 5.5 26.7 12.7 35.3 30.5 10.0 24.1
6 ”6:03; 55.6 5.0 32.9 6.5 37.0 28.5 13.5 21.0
V V { =47.1,Vp= .000*** _ { 540.9, p5 .000***
CA 2 hours or less 44.7% 9.1 28.6 17.7 17.1% 40.5 6.9 35.4
3-5 hours 54.4 9.6 26.7 9.3 23.8 39.8 7.2 29.2
6 “66:0: : 52.3 5.1 35.7 6.9 24.8 38.7 15.1 21.4
{ =36.2, p= .000*** { 535.9, p= .000***
. Activities
us 2 hours or less 18.6% 9.4 14.8 57.2 2.8% 14.2 2.0 80.9
3-5 hours i 24.5 8.2 21.9 45.4 5.3 17.7 4.8 72.3
6 “66:03; i 29.7 6.0 26.4 37.9 7.0 20.0 4.8 68.3
f {547.4, p= 000*" . {523.8, p5 .001**
CA 2 hours or less . 19.0% 11.6 16.7 52.7 1.4% 16.2 1.8 80.6
3-5 hours 27.8 10.2 20.1 41.9 2.7 21.3 3.0 73.1
6 “”30: 30.3 6.3 30.0 33.4 5.4 16.5 4.8 73.2
V.VV..x2750-0.P= 0002‘- , . x2=21-3..P= 002”
: Attractions
US 2 hours or less 25.4% 9.8 25.4 39.3 3.2% 16.4 2.6 77.7
3-5 hours 33.9 9.9 33.4 22.8 5.3 19.3 5.0 70.4
6 hours or ‘
more , 37.4 9.5 41.1 12.0 6.5 25.5 6.3 61.8
{ 593.7, p= .000*** { =28.9, p5 .000***
CA 2 hours or less ~ 27.1% 12.1 28.3 32.5 1.4% 21.9 2.8 73.9
3-5 hours 28.0 10.8 32.0 19.2 4.2 26.3 3.3 66.2
6 hours or
more i 38.6 7.1 42.6 11.7 3.4 23.6 4.3 68.7
. { =66.2,p= .000*** { 510.3, p= .112
Car Rentals
us 2 hours or less 18.2% 2.0 7.6 72.1 12.6% 7.7 1.2 78.5
3-5 hours * 25.5 6.3 9.1 59.1 21.0 11.2 2.1 65.6
6 ”6:03; 34.7 7.7 9.2 48.4 25.5 12.3 4.5 57.8
. Vx’ =6o.1. p=_..000*** ‘ V . x’=49.5._p= .000***
CA 2 hours or less 10.7% 7.2 4.6 77.5 7.6% 9.5 0.9 82.0
3-5 hours ; 15.2 6.3 3.6 74.9 9.0 11.7 2.1 77.2
6 "66:03; 22.6 8.3 12.9 56.3 12.6 18.3 2.6 66.6

{ =58.9, p5 .000***

* p < .05; ** p < .01; and p < .001

95

{26.7, p5 .000***

 

Table 4-17

Travel Information Search and Travel Product Purchase Inﬂuenced by Time
Spent Online for the Latest Trip (continued)

 

Travel Information Search

Travel Product Purchase

 

Time spent online On/ On/
for the latest trip Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Events
us 2 hours or less 20.3% 7.0 17.4 55.3 4.7% 12.8 2.6 79.9
3-5 hours ; 27.9 7.2 21.4 43.5 7.6 17.2 3.6 71.6
6 "66:0‘; 35.9 5.0 23.7 35.4 8.0 12.0 4.5 75.5
‘ ‘ { =45.6, p= .000*** { =13.6, p= 034*
CA 2 hours or less 23.0% 10.4 18.8 47.8 3.7% 15.9 2.5 77.9
3-5 hours 33.2 7.8 18.6 40.4 5.1 18.6 2.7 73.6
6 hours or
more 29.1 8.3 23.1 39.4 4.3 15.7 3.7 76.3
{ 515.3, p= 018* { 53.5, p5 .749
Flights
US 2 hours or less ‘ 40.8% 1.8 9.4 48.0 31.6% 8.7 3.0 56.6
3-5 hours 43.8 5.3 14.4 36.5 36.3 14.6 5.0 44.2
6 “66:03; 49.1 7.2 18.2 25.4 33.0 24.8 7.5 34.8
~ { =60.6, p= .**000 . { =70.6, p5 .000***
CA 2 hours or less 27.9% 4.9 14.7 52.6 14.8% 19.9 3.7 61.6
3-5 hours 28.8 9.0 15.6 46.5 . 15.0 24.9 3.9 56.2
6 ”6:06; ‘ 30.2 13.7 25.6 30.5 14.3 40.0 7.1 38.6

' { 554.2, p5 .000***

4 p < .05; " p < .01; and *** p < .001

96

1‘ { =55.6, p5 .000***

 

Travel Information Search and Travel Product Purchase Inﬂuenced by
Latest Trip Behavior

Length of stay of the latest tn'p signiﬁcantly differed in regard to information
search for activities, attractions, car rentals and ﬂights in the U.S., and all six
products in Canada (Table 4-18). US respondents who stayed longer on their
latest trip were more likely to search for information onlofﬂine on
accommodations, activities, car rentals and ﬂights, and ofﬂine on ﬂights. Canada
respondents who stayed longer on their latest trip were more likely to search for
information onlofﬂine on accommodations, activities, attractions, car rentals and
events, and online and ofﬂine on car rentals and ﬂights.

Length of stay of the latest tn'p signiﬁcantly differed in purchasing
accommodations, activities, car rentals and ﬂights in the U.S., and
accommodations, car rentals, events and ﬂights in Canada (Table 4-18). US
respondents who stayed longer on their latest trip were more likely to purchase
activities online, car rentals and ﬂights ofﬂine, and activities and ﬂights onlofﬂine.
Canada respondents who stayed longer on their latest trip were more likely to

purchase ﬂights online, car rentals and ﬂights ofﬂine, and car rentals onlofﬂine.

97

Table 4-18
Travel Information Search and Travel Product Purchase Inﬂuenced by
Length of Stay of the Latest Trip

 

 

, Travel Information Search Travel Product Purchase
Length of stay of the . On/ On!
latest trip ~ Online Ofﬂine ofﬂine None Online Ofﬂine Ofﬂine None
} Accommodations
US 1-2 nights , 60.2% 6.6 22.3 10.8 , 35.1% 34.5 5.4 25.0

3-6 nights ' 54.3 4.6 26.8 14.3 i 36.5 29.4 6.9 27.2
7-13 nights 47.6 7.1 29.3 16.0 -31.5 27.7 12.1 28.7

14 ”29%;; ~ 46.9 5.9 34.3 13.0 31.7 24.6 13.3 30.4
, w . x’=153.p=.050 V x’=2o.s.p=.o15*
CA 1-2 nights a 65.2% 5.1 22.5 72 23.2% 49.3 6.5 21.0

3—6 nights ’ 54.4 6.4 28.1 11:0 3 26.7 41.1 4.9 27.3
7-13 nights 42.9 9.7 32.1 15.3 ? 16.7 37.3 10.7 35.3

‘4 "292:3; g 46.7 9.2 33.7 10.5 21.2 35.8 14.7 28.3
. { 527.7, p= .001** . { =38.7, p= .000***
:Actlvltles
us 12 nights 17.5% 8.4 17.5 56.6 3.6% 13.1 1.8 81.5

3-6 nights 25.4 5.7 18.0 50.9 ' 4.8 14.0 3.1 78.2
7-13 nights . 25.4 8.1 22.1 44.3 1 5.0 20.9 3.8 70.3

‘4 "igwof; 21.8 11.7 26.4 40.2 i 5.4 20.8 6.7 67.1
e, ., A.fx’525.,4.p=.052**w x’523.3.p=.005**
CA 12 nights 22.5% 8.7 11.6 57.2 2.2% 15.1 0.7 82.0

3-6 nights ; 25.1 6.7 17.4 50.8 _ 1.5 18.7 3.1 76.7
7-13 nights ? 29.7 9.9 23.2 37.1 4.5 16.9 2.8 75.8

14nig'rogl219 12.1 29.7 36.3 ‘ 3.6 19.5 4.2 72.6
x’=452. p= .000***V V x2=12.0. p5 .214
Attractions
us 12 nights ; 27.1% 8.4 29.5 349 : 3.6% 14.3 3.6 78.6

3-6 nights 3 34.6 7.5 28.3 29:6 ' 5.4 18.5 3.6 72.5
7-13 nights 3 33.6 11.2 34.1 21.1 ' 5.0 21.9 4.8 68.3

‘4 "igﬁsog 25.5 13.4 42.7 18.4 3 4.2 25.8 6.3 63.8
, , , ix’=40.9.p_=1000*** n , H Vx’515.6.p=.075
CA 12 nights _ 29.7% 10.9 26.8 32.6 1.4% 20.3 2.9 75.4

36 nights 36.6 10.1 28.0 25.3 2.8 23.9 3.4 69.9
7-13 nights r 36.4 6.8 35.5 21.3 ’ 3.1 22.5 2.5 71.8
14 ”mm: _ 30.1 13.4 42.5 14.1 ‘ 3.3 27.0 4.6 65.1
{=40.3, p= .000*** { 57.4, p= .596
* p < .05; ** p < .01; and **" p < .001

98

Table 4-18

Travel Information Search and Travel Product Purchase Inﬂuenced by
Length of Stay of the Latest Trip (continued)

. Travel lnforrnation Search

' Travel Product Purchase

 

Length of stay of the ; On/ On/
latest trip Online Ofﬂine ofﬂine None Online Ofﬂine Ofﬂine None
Car Rentals
us 12 nights , 16.9% 1.2 6.0 75.9 x 12.5% 2.4 1.8 83.3
3-6 nights 22.6 4.0 8.6 64.8 17.2 8.8 1.5 72.5
7-13 nights 5 30.5 3.8 8.9 56.7 25.2 10.8 2.8 61.2
‘4 ”waif; " 30.1 12.1 10.0 47.7 i 17.9 17.9 5.0 59.2
§_ , V 18550.5. p=,.ooo*** ,,,,,, 1 x’=58.8. p: .000***
CA 12 nights. 8.6% 2.2 2.2 87.1 2.9% 6.5 0 90.6
36 nights 11.6 3.4 4.6 80.4 9.8 7.7 0.6 81.8
7-13 nights 16.5 8.8 8.2 66.5 7.0 13.5 2.0 77.5
14 "'92:;3; ; 22.9 11.8 9.8 55.6 N 15.3 20.5 3.6 60.6
{ 571.9, p5 .000*** : { 570.1, p5 .000***
V Events
us 1-2 nights 30.7% 7.2 17.5 44.6 7.1% 14.3 2.4 76.2
36 nights 2 31.0 6.1 20.3 42.6 8.6 13.8 3.3 74.4
7-13 nights 23.7 7.6 19.6 49.1 6.0 14.6 3.0 76.3
‘4 "'g'xfof; V 24.3 5.0 26.4 44.4 ‘ 2.9 12.5 5.8 78.8
. e. .V {514745.109 w M . ++ + 1515915126
CA 12 nights a 30.4% 10.1 16.7 42.8 8.7% 13.8 1.4 76.1
3—6 nights i 32.7 8.0 19.9 39.4 5.2 18.7 3.7 72.4
7-13 nights 29.2 5.9 20.1 44.8 4.2 13.5 2.0 80.3
14 mm: x 20.3 12.8 22.6 44.3 _ 1.3 19.9 3.6 75.2
, 'x2=21-8..P= 1010* {5255.15 .005**
. Flights '
us 12 nights 27.7% 0.6 6.6 65.1 20.8% 2.4 3.0 73.8
36 nights a 46.7 1.9 10.9 40.5 : 34.8 10.1 3.3 51.8
7-13 nights 47.8 5.9 15.5 30.8 29.8 18.6 5.8 35.8
‘4 "'me; ' 43.1 12.1 21.8 23.0 ;_ 28.3 31.3 9.2 31.3
m _ 1651295. p: .000*** w_ Vx’ =149.9. p= .000*** _
CA 12 nights V; 15.2% 2.9 3.6 78.3 f 7.3% 5.8 1.5 85.4
3-6 nights f 28.7 4.9 12.5 54.0 3 16.0 15.0 4.3 64.7
7-13 nights 30.6 9.9 26.1 33.4 : 16.7 36.4 3.7 43.2
‘4 "'g'fog : 34.1 14.4 22.3 29.2 f 14.3 39.9 8.4 37.3

: {5139.9, p= .000***

* p < .05; ** p < .01; and ***p < .001

99

{ 5144.7, p= .000***

Destination of the latest tn'p signiﬁcantly differed in regard to lnforrnation
search for accommodations, activities, attractions, car rentals and ﬂights in the
U.S., and accommodations, activities, car rentals and ﬂights in Canada (Table 4-
19). Respondents (both US and Canada) who traveled out of state/province
were more likely to search for information on ﬂights online, ofﬂine and onlofﬂine
than not search for information. US respondents who traveled farther were more
likely to search for information on attractions ofﬂine and onlofﬂine.

Destination of the latest trip signiﬁcantly differed in purchasing activities,
attractions and ﬂights in the U.S. and Canada (Table 4-19). Respondents (both
US and Canada) were less likely to purchase ﬂights when traveling within state.
US respondents who traveled farther were more likely to purchase ﬂights ofﬂine
and onlofﬂine. Canada respondents who traveled out of province were more
likely to purchase ﬂights ofﬂine.

Season of the latest trip signiﬁcantly differed in regard to information search
for accommodations, activities, attractions, events and ﬂights in the U.S., and
activities, attractions, car rentals and ﬂights in Canada (Table 4-20). Season
signiﬁcantly differed in purchasing activities, car rentals and ﬂights in the U.S.,
and accommodations, car rentals and ﬂights in Canada. However, no speciﬁc

patterns in the data were found.

100

Table 4-19

Travel Information Search and Travel Product Purchase Inﬂuenced by
Destination of the Latest Trip

 

Travel Information Search

Travel Product Purchase

 

Destination of the On] On/
latest trip Online Ofﬂine ofﬂine None Online Ofﬂine Ofﬂine None
Accommodations
US Within state . 54.0% 4.2 28.8 13.0 35.9% 28.2 9.5 26.4
Out of state, - 48.2 5.8 27.6 18.3 32.3 27.3 8.9 31.5
within country .
Out of country, 55.7 5.9 30.3 8.0 37.3 31.4 9.8 21.6
within North
America
Out of North 52.5 8.3 26.5 12.7 30.8 29.7 10.4 29.1
America
f f . {=21.6,p= 010* f _ {=13.1,p= .159
CA Within state 59.7% 5.8 28.7 5.8 21.7% 47.3 7.4 23.6
Out of state, ; 51.7 6.5 27.2 14.6 ; 23.3 32.0 11.2 33.5
within country 5 ?
Out of country, 44.1 7.0 34.1 14.7 18.2 41.7 10.6 29.5
within North
America
Out of North 44.6 13.6 31.4 10.3 23.1 38.4 8.3 30.2
America
; { 536.2, p= .000*** { 57.5, p5 .585
Activities
US Within state 1 23.2% 7.0 18.9 50.9 4.6% 18.7 3.9 72.9
Out of state, » 18.3 8.1 19.5 54.0 3.8 13.7 3.0 79.5
within country
Out of country, . 34.8 5.6 20.6 39.0 7.3 18.1 2.8 71.8
within North
America
Out of North 24.3 12.2 27.1 36.5 4.4 24.2 7.7 63.7
America V
A , , 50517545000” ..... x’=31.5.p=.000***
CA Within state ‘ 26.7% 8.9 19.4 45.0 2.3% 17.1 1.6 79.1
Out of state, 26.9 9.6 23.5 39.9 2.8 21.4 2.8 73.0
within country
Out of country, 21.1 5.7 21.1 52.2 2.7 14.6 3.3 79.4
within North
America
Out of North 27.2 14.4 23.0 35.4 4.1 17.8 4.5 73.6
America -

{ 525.2, p5 .003**

* p < .05; “ p < .01; and m p < .001

101

{ 523.2, p5 .006**

Table 4-19

Travel Information Search and Travel Product Purchase Inﬂuenced by
Destination of the Latest Trip (continued)

 

Travel Information Search

Travel Product Purchase

 

Destination of the I On/ On/
latest trip Online Ofﬂine ofﬂine None Online Ofﬂine Ofﬂine None
2 Attractions
US Within state 33.7% 8.8 28.8 28.8 7.0% 19.7 3.5 69.7
Out of state, 3 28.5 9.0 32.0 30.5 3.7 19.3 4.7 72.3
within country
Out of country, V 35.9 11.1 33.1 19.9 5.2 20.2 4.9 69.7
within North '
America
Out of North . 32.0 11.6 40.9 15.5 4.4 24.7 4.4 66.5
America :
V_ ._ .x ’-279 155001“ , x2=271 p= 001** ,
CA Within state ; 34.5% 8. 5 31.0 26.0 2. 7% 22. 3 1.9 73.1
Out of state, j 36. 8 10. 8 31.6 20.7 3.1 27.2 4.6 65.0
within country ;
Out of country, 7 30.8 9.4 36.1 23.7 3.6 22.5 3.3 70.5
within North :
America
Out of North . 33.5 11.2 38.8 16.5 1.7 22.7 3.7 71.9
America ;
..... .x2 =120 P. 211 x2523-6.P= 0052*.
; Car Rentals
US Within state i 22.5% 5.6 6. 0 66.0 14.1% 7.7 1.4 76.8
Out of state, ; 25.2 2.3 11 .6 60.9 * 21.7 8.7 3.1 66.4
within country : ‘
Out of country, 2 30.0 7.7 6.6 55.7 21.6 12.5 2.1 63.8
within North 5
America :
Out of North 24.9 8.8 6.6 59.7 14.8 15.4 3.3 66.5
America I 1
w , x -339.p- 000*" , 'x2=1.6.P=-996
CA Within state : 5. 0% 3.1 2. 3 89.6 V 2.7% 6.6 0 90.7
Out of state, V 16. 0 7. 7 8. 3 67.9 ‘ 13.0 13.7 1.2 72.0
within country Q
Out of country, ‘ 22.4 5.4 7.0 65.2 ' 11.3 13.9 3.0 71.9
within North :
America 5
Out of North ' 18.6 13.2 9.9 58.3 , 10.3 17.3 2.5 70.0
America : ‘
{ =80.5, p= .000*** { 515.4, p5 .080

 

'p < .05; **p< .01; and ”*p < .001

102

Table 4-19
Travel Information Search and Travel Product Purchase Inﬂuenced by
Destination of the Latest Trip (continuep)

 

Travel lnfonnation Search

Travel Product Purchase

 

Destination of the On/ On/
latest trip Online Ofﬂine ofﬂine None Online Ofﬂine Ofﬂine None
Events
US Within state 25.6% 7.7 19.6 47.0 7.4% 13.0 2.8 76.8
Out of state, V 27.1 6.2 21.3 45.4 7.8 13.6 4.0 74.6
within country
Out of country, 1 29.3 4.9 20.9 44.9 5.9 15.3 3.5 75.3
within North
America
Out of North 29.3 8.3 21.0 41.4 2.7 13.7 3.3 80.2
America
I { =4.8, p5 .854 { 54.7, p5 .862
CA Within state 27.0% 5.8 19.3 47.9 5.0% 15.1 3.1 76.7
Out of state, 30.7 7.4 19.8 42.1 4.0 19.6 2.8 73.6
within country :
Out of country, 28.2 10.1 19.8 41.9 4.3 17.2 2.6 75.8
within North
America
Out of North 24.7 12.8 22.6 39.9 3.3 14.5 2.9 79.3
America
I {512.9, p5 .166 {516.1, p5 .064
. Flights
US Within state - 32.6% 1.4 11.9 54.0 1 23.6% 9.2 3.2 64.1
Out of state, : 46.3 2.8 12.7 38.2 , 38.1 9.4 4.7 47.8
within country .
Out of country, 46.3 6.6 15.0 32.1 32.4 23.7 5.6 38.3
within North
America
Out of North 50.8 13.3 17.7 18.2 35.2 31.9 8.2 24.7
America 1
m ,.. ,. .. 185955.. p= .000*** 'x’=17.9,p= 037*
CA Within state . 10.9% 2.7 6.2 80.2 1 3.5% 7.4 1.2 88.0
Out of state, 33.4 5.0 13.9 47.7 19.9 18.0 5.6 56.5
within country
Out of country, 33.8 10.7 21.7 33.8 16.2 36.8 3.6 43.4
within North .
America 2
Out of North 37.6 18.2 32.6 11.6 17.7 49.8 9.1 23.5
America

' { =278.5, p= .000***

1 {558.5495 .000***

 

* p < .05; ‘* p < .01; and m p < .001

103

Table 4-20
Travel Information Search and Travel Product Purchase Inﬂuenced by
Season of the Latest Trip
Travel lnfonnation Search

Travel Product Purchase

 

Season ofthe On] On/
latest trip Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Accommodations
US Winter 52.4% 2.4 25.0 20.2 38.1% 25.0 8.3 28.6
Spring 46.1 7.9 33.1 12.9 31.5 31.5 10.5 26.5
Summer 55.0 5.6 29.8 9.6 36.2 30.0 10.7 23.1
Fall 50.7 5.9 26.2 17.2 32.4 27.3 8.2 32.0
{ 521.4, p5 011* { 510.9, p5 .284
CA Winter 46.4% 5.2 39.2 9.3 19.6% 43.3 8.2 28.9
Spring 44.9 9.6 30.1 15.4 23.9 32.7 8.8 34.6
Summer 52.1 7.8 30.4 9.8 20.5 42.7 9.8 27.0
Fall 50.7 8.0 28.0 13.2 22.3 37.6 10.0 30.1
_ {510.8, p= .293 { 519.1, p5 024*
Activities
US Winter 22.6% 3.6 26.2 47.6 6.0% 16.7 1 1.9 65.5
Spring 21.3 9.0 19.1 50.6 2.8 18.2 6.1 72.9
Summer 24.4 9.0 25.5 41.1 6.4 19.3 3.2 71.1
Fall 24.2 7.3 16.6 52.0 4.0 15.3 2.3 78.4
I _ . x2 =223 p= .008" , , :12 =29.0. p= .001**
CA Winter 35.1% 9.3 18.6 37.1 5.2% 21.9 4.2 68.8
Spring 30.6 7.6 21.7 40.1 3.8 15.8 3.8 76.6
Summer 27.5 9.8 25.6 37.1 3.1 22.5 3.3 71.2
Fall 18.3 10.0 18.5 53.2 2.2 12.6 1.9 83.3
_{x’=35.o..p=_..905*** ‘ V ,, H ‘ x’=11.o.p=.278
Attractions
US Winter 35.7% 2.4 33.3 28.6 2.4% 17.9 8.3 71.4
Spring 33.1 7.9 29.8 29.2 4.4 24.3 5.0 66.3
Summer 31.5 11.1 37.7 19.7 7.1 24.4 3.9 64.7
Fall 30.9 10.3 29.7 29.1 3.5 16.3 4.2 76.0
{ 522.9, p= .006** { =8.3, p= .506
CA Winter 42.9% 5.1 29.6 22.4 2.1% 25.8 4.1 68.0
Spring 40.5 7.0 31.0 21.5 4.4 20.9 5.1 69.6
Summer 34.1 11.7 35.0 19.1 2.8 29.4 3.9 63.8
Fall 28.9 10.8 35.5 24.9 2.4 18.4 1.9 77.2
. { 517.5, p5 041* { 58.9, p5 .443
Car Rentals
US Winter 36.9% 3.6 8.3 51.2 21 .4% 10.7 3.6 64.3
Spring 21.3 5.1 11.2 62.4 19.3 9.9 2.8 68.0
Summer 27.2 5.8 9.6 57.4 18.8 11.1 2.4 67.7
Fall 24.0 4.7 7.1 64.2 19.0 9.6 2.5 69.0
H ‘ w .x2=14-4.P:-108 , ,x’=24.8.p=.003**
CA Winter 22.4% 12.2 7.1 58.2 ; 8.2% 19.6 3.1 69.1
Spring 19.1 5.7 10.8 64.3 ‘ 13.3 15.2 3.2 68.4
Summer 11.1 7.0 5.9 76.1 7.6 12.0 1.3 79.0
Fall 18.3 6.8 6.6 68.3 10.5 11.4 1.5 76.6

{ 525.5415 .002**

" p < .05; " p < .01; and *** p < .001

104

{ =48.1, p5 .000***

Table 4-20

Travel Information Search and Travel Product Purchase Inﬂuenced by
Season of the Latest Trip (continued)
Travel Information Search

Travel Product Purchase

 

Season of the On/ On/
latest trip Online Ofﬂine ofﬂine None Online Ofﬂine offline None
‘Events
US Winter 33.3% 2.4 19.0 45.2 7.1% 15.5 6.0 71.4
Spring 19.7 9.0 19.1 52.2 5.5 13.8 2.2 78.5
Summer 30.2 5.1 24.6 40.0 7.5 13.7 3.0 75.8
Fall 27.0 7.4 18.6 47.0 . 6.2 13.8 4.0 76.0
x2.=22-.5..p=.-.008iﬂ,1.. H ,x?‘=7.‘.3.p=.545W
CA Winter 32.7% 9.2 12.2 45.9 3.1% 17.5 1.0 78.4
Spring 33.8 8.9 17.2 40.1 3.2 14.6 4.4 77.8
Summer 25.0 8.9 21.3 44.8 3.1 20.3 3.1 73.6
Fall 27.9 9.0 22.2 40.8 6.3 13.6 2.4 77.6
x2 510.3, p5 .329 x2 54.3, p5 .892
Flights
US Winter 53.6% 9.5 14.3 22.6 234.5% 20.2 7.1 38.1
Spring 41.6 7.9 18.0 32.6 735.9 19.9 7.2 37.0
Summer 40.9 3.9 15.2 40.0 29.3 15.4 4.9 50.3
Fall 45.8 3.9 11.1 39.2 35.6 13.6 4.2 46.6
x2=26-1.,P=.-002",, ...... x25125.4.p=.ooo"*
CA Winter ~. 36.1% 13.4 23.7 26.8 g 17.3% 39.8 6.1 36.7
Spring 32.1 10.3 28.2 29.5 15.2 41.1 7.6 36.1
Summer ; 23.3 7.8 15.0 53.9 10.9 21.1 4.4 63.6
Fall 32.9 8.3 17.1 41.7 18.2 26.3 4.1 51.3

12 550.7, p5 .000***

, x2 5255.0, p5 .000***

 

* p < .05; ** p < .01; and *** p < .001

105

Summary of Testing Hypotheses One to Ten

Hypotheses one to ten were aimed at understanding which elements
signiﬁcantly differed in regard to travel information search and travel product
purchase behaviors during pretrip. In the ﬁrst part, US and Canada samples
were compared on general background, travel planning behavior during pretrip
and trip speciﬁc behavior during tn’p. These comparison studies between US and
Canada examined whether country of residence signiﬁcantly affected other
independent variables. Hypotheses 1a and 4a were tested in the ﬁrst part of
chapter 4. The second part showed the results of testing Hypotheses 1b to 100
(excluding 4a). Independent variables, such as general background, lntemet use
for the latest trip and trip speciﬁc behavior during tn'p, were tested on travel
information search behaviors and travel product purchase behaviors across six
travel products.

To test Hypotheses one to ten, cross-tabulation and Chi-square were used to
examine the statistical signiﬁcance of differences on relationships between
variables. Signiﬁcance of difference of Hypotheses 1a and 4a was measured
between US and Canada respondents. Signiﬁcance of difference of Hypotheses
1b to 3d and 4b to 100 was measured on travel information search behaviors or
travel product purchase behaviors with nominal scales, including the four
category responses of online, ofﬂine, on/ofﬂine and none.

Summary of testing hypotheses one to ten shows results of Chi-square tests,
speciﬁcally, whether hypotheses were accepted, partially accepted or not (Table

4-22). To accept a hypothesis, independent variables must signiﬁcantly differ in

106

regard to travel information search or travel product purchase on all six travel
products. Othenivise, hypotheses were partially accepted (on some of the six

products) or rejected (no products).

Hypotheses Acceptance

Hypotheses 1a to 1d were partially accepted. Country of residence
signiﬁcantly differed in regard to travel lnfonnation search for car rentals and
ﬂights. Gender signiﬁcantly differed in regard to travel information search for ﬁve
products (accommodations, activities, attractions, car rentals and ﬂights) in the
U.S., and it signiﬁcantly differed for two products (events and ﬂights) in Canada.
Age signiﬁcantly differed on travel information search for three products
(accommodations, activities and ﬂights) in the U.S. Age signiﬁcantly differed on
travel information search for four products (accommodations, activities, ﬂights
and events). Education signiﬁcantly differed on travel information search for
three products (activities, car rentals and ﬂights) the U.S., but more in Canada
(ﬁve products: activities, attractions, car rentals, events and ﬂights).

Hypothesis 2a was partially accepted. Number of vacation trips in recent 12
months Signiﬁcantly differed on travel information search for four products
(accommodations, activities, attractions and car rentals) in the U.S., but only one
product (car rentals) in Canada.

Hypothesis 3b was rejected in the U.S., but partially accepted in Canada.
Time spent online per week did not signiﬁcantly differ on travel information

search for any of the products in the U.S., but only one (accommodations) in

107

Canada. Hypotheses 3a, 3c and 3d were partially accepted. Years of lntemet
use signiﬁcantly differed on travel information search for ﬁve products
(accommodations, activities, attractions, events and ﬂights) in Canada, but only
one (ﬂights) in the U.S. Speed of lntemet connection signiﬁcantly differed on
travel information search for four products (accommodations, activities, car
rentals and ﬂights) in the U.S. but two products (activities and car rentals) in
Canada. Credit card use for online purchase Signiﬁcantly differed on travel
information search for accommodations and ﬂights the U.S. and car rentals and
ﬂights in Canada.

Hypotheses 4a to 4d, which tested travel product purchase behaviors, were
partially accepted. Country of residence signiﬁcantly differed in purchasing
accommodations, attractions, car rentals, events and ﬂights. Gender signiﬁcantly
differed on purchase of car rentals and ﬂights in the U.S. and accommodations
and car rentals in Canada. Age signiﬁcantly differed in purchasing three
products (activities, attractions and events) in Canada, but only one (activities) in
the U.S. Education signiﬁcantly differed in purchasing three products
(attractions, car rentals and ﬂights) in the U.S. and two products (activities and
car rentals) in Canada.

Hypothesis 5a was partially accepted. Number of vacation trips in recent 12
months signiﬁcantly differed in purchasing three products (accommodations, car
rentals and ﬂights) in the U.S. and two (activities and events) in Canada.

Hypothesis 6c was rejected in Canada. Speed of lntemet connection did not

signiﬁcantly differ in regard to travel product purchase in Canada, but it

108

signiﬁcantly differed in purchasing four products (accommodations, activities, car
rentals and ﬂights) in the U.S. Hypothesis 6d was accepted in Canada. Credit
card use for online purchase signiﬁcantly differed in purchase of all six travel
products in Canada. Credit card use Signiﬁcantly differed in purchasing four
products (accommodations, car rentals, events and ﬂights) in the U.S.
Hypotheses 63 and 6b were partially accepted. Years of lntemet use
signiﬁcantly differed in purchasing ﬂights in the U.S. and car rentals in Canada.
Time spent online per week signiﬁcantly differed in purchaSing ﬂights in the U.S.
and events in Canada.

Hypothesis 7a, which returned to travel lnfonnation search behaviors, was
partially accepted. Planning horizon for lntemet use for the latest tn'p signiﬁcantly
differed on travel information search for four products (activities, attractions, car
rentals and ﬂights) in the U.S. and ﬁve products (accommodations, activities,
attractions, car rentals and ﬂights) in Canada. Hypothesis 7b was accepted in
both the U.S. and Canada. Time spent online for the latest trip signiﬁcantly
differed on travel lnfonnation search for all six products in the U.S. and Canada.

Hypothesis 8a, which addressed travel product purchasing, was partially
accepted. Planning horizon for lntemet use for the latest trip signiﬁcantly differed
in purchasing four products (accommodations, activities, car rentals and ﬂights)
in the U.S., and three products (accommodations, car rentals and ﬂights) in
Canada. Hypothesis 8b was accepted in the U.S., and partially accepted in

Canada. Time spent online for the latest tn’p signiﬁcantly differed in purchasing

109

all six products in the U.S., and four products (accommodations, activities, car
rentals and ﬂights) in Canada.

Hypothesis 9a, which returned to travel lnfonnation search behaviors, was
accepted in Canada, and partially accepted in the U.S. Length of stay of the
latest tn'p signiﬁcantly differed on travel information search for all six products in
Canada and four products (activities, attractions, car rentals and ﬂights) in the
U.S. Hypotheses 9b and 9c were partially accepted. Destination of the latest tn'p
signiﬁcantly differed on travel information search for ﬁve products
(accommodations, activities, attractions, car rentals and ﬂights) in the U.S. and
four products (accommodations, activities, car rentals and ﬂights) in Canada.
Season of the latest tn'p signiﬁcantly differed on travel information search for ﬁve
products (accommodations, activities, attractions, events and ﬂights) in the U.S.
and three products (activities, attractions and ﬂights) in Canada.

Hypothesis 10a, which addressed travel product purchasing, was partially
accepted. Length of stay of the latest trip signiﬁcantly differed in purchasing four
products (accommodations, activities, car rentals and ﬂights) in the U.S. and four
products (accommodations, car rentals, events and ﬂights) in Canada.
Hypotheses 10b and 100 were partially accepted. Destination of the latest trip
Signiﬁcantly differed in purchasing three products (activities, attractions and
ﬂights) in the U.S. and Canada. Season of the latest tn'p signiﬁcantly differed in
purchasing products (car rentals and ﬂights) in the U.S. and three products

(accommodations, car rentals and ﬂights) in Canada.

110

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114

Association Between Travel lnfonnation Search and Travel Product
Purchase

Finally, hypothesis 11 was tested (Travel information search behaviors and
travel product purchase behaviors are associated, however, not perfectly).
Contingency Coefﬁcient analysis was conducted to compare the magnitude of
association between travel information search and travel product purchase
across six travel products. Cross-tabulation was also use to examine
interrelationships between travel information search and travel product purchase
by six travel products. A Contingency Coefﬁcient estimate ranges from zero to
one, with zero corresponding to no association and one to perfect association
(Norusis, 2002). The largest value it can have depends on the number of rows
and columns in the tables (Norusis, 2002). Since this data had four by four
. tables because of four categories in the nominal scale, online, ofﬂine, onlofﬂine
and none, the largest possible value of C is 0.87 (Norusis, 2002).

The highest association was found for car rental information search and
purchase (C=.64 for US respondents; C=.63 for Canada), followed by ﬂight
information search and purchase (C=.63 for US; C=.63 for Canada) (Tables 4-23
to 4-28). The association between travel information search and product
purchase on car rentals was strongest with online behaviors of US respondents,
with 60 percent of those who searched for car rental information online and
purchased online. Canada respondents also showed strong usage of the
Internet for information search and purchasing car rentals, with 49 percent of
those who searched for online and also purchased online. The next highest

association was for another transportation mode (ﬂights). Greater numbers of

115

these Internet users who were studied searched for information and purchased
ﬂights on their latest trip (65 percent of US respondents and 41 percent of
Canada respondents).

On other product categories, Contingency Coefﬁcients ranged from .49 for
accommodation information searching and purchasing by US respondents
and .47 by Canada respondents, to lower levels of associations for travel product
purchases of activities, attractions and events. These coefﬁcients ranged from a
low of .21 for Canada respondents and attraction products to .37 for Canada
respondents and event products.
Table 4-22

Association of Travel Information Search and Travel Product Purchase
during Pretrip on Accommodations

 

 

 

Travel Product Purchase
Travel Q Canada
lnfonnation . , 0n] , ,

Search Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Online 26.1%a 13.1 4.3 8.3 17.1%a 18.9 5.6 8.6
Ofﬂine 0.2 2.6 0.3 2.7 0.4 4.0 0.2 3.4

On/ofﬂine 7.0 10.9 4.6 5.7 3.8 15.1 3.7 7.5
None 0.8 1.9 0.2 11.2 0.1 1.5 0.1 10.0
C: .49 C: .47

 

 

 

a. Total percent reported

Table 4-23
Association of Travel lnfonnation Search and Travel Product Purchase

during Pretrip on Activities

 

 

Travel Product Purchase
Travel U_S Canada
Information . . On/ . . On/

Search Online Ofﬂine offline None Online Ofﬂine ofﬂine None
Online 3.0% a 4.9 1.3 14.7 1.2% a 6.0 1.1 17.0
Ofﬂine 0.2 1.9 0.2 5.5 0.4 2.3 0.4 6.4

On/ofﬂine 0.8 5.9 1.7 12.2 0.5 5.3 1.2 15.0
None 0.8 4.3 0.5 41.9 0.6 4.4 0.5 37.6
C: .33 C: .23

 

 

 

a. Total percent reported

116

Table 4-24
Association of Travel Information Search and Travel Product Purchase
during Pretrip on Attractions

 

 

Travel Product Purchase
f Travel u_s Canada
In ormation , _ On/ . . On/

Search Online Ofﬂlne offline None Online Offline ofﬂine None
Online 3.2% a 5.9 1.4 21.0 1.3% a 9.9 1.3 21.4
Ofﬂine 0.2 2.2 0.1 7.4 0.4 2.7 0 7.1

On/ofﬂine 1.3 10.3 2.7 18.6 0.8 8.9 1.8 22.6
None 0.2 1.7 0.2 23.6 0.4 2.3 0.4 18.8
C= .33 C: .21

 

 

 

a. Total percent reported

Table 4-25
Association of Travel lnfonnation Search and Travel Product Purchase
during Pretrip on Car Rentals

 

 

Travel Product Purchase
Travel _Lﬁ Canada
Information , . On/ . . On/

Search Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Online 15.3% a 3.5 0.8 5.9 7.7% a 4.1 0.4 3.5
Ofﬂine 0.5 2.7 0.1 1.7 0.5 3.1 0.3 3.3

On/ofﬂine 2.3 2.1 1.4 3.0 0.9 2.9 0.8 2.2
None 1.1 1.7 0.2 57.6 0.4 2.8 0.2 66.8
C= .64 C: .63

 

 

 

a. Total percent reported

Table 4-26
Association of Travel Information Search and Travel Product Purchase
during Pretrip on Events

 

 

Travel Product Purchase
Travel u_s Canada
Information . . On/ . . On/

Search Online Ofﬂine ofﬂine None Online Ofﬂine ofﬂine None
Online 3.6% a 4.5 1.0 18.5 3.0% a 6.5 0.8 175
Ofﬂine 0.3 1.4 0.3 4.6 0 2.2 0.2 6.6

On/ofﬂine 2.1 5.2 2.1 11.6 1.0 5.4 1.7 12.1
None 0.7 2.7 0.2 41.4 0.2 2.7 0.3 39.7
C: .36 C: .37

 

 

 

a. Total percent reported

117

Table 4-27
Association of Travel Information Search and Travel Product Purchase
during Pretrip on Flights

 

 

Travel Product Purchase
Travel Ll§ Canada
Information . . On/ . . On/

Search Online Ofﬂine ofﬂine None Online Offline ofﬂine None
Online 28.4% a 5.5 2.4 7.6 12.0% a 7.9 2.8 6.6
Ofﬂine 0.2 3.3 0.4 0.9 0.4 5.7 0.3 2.5

On/ofﬂine 3.2 4.5 1.9 4.0 1.8 10.8 1.8 4.0
None 1.6 2.2 0.2 33.7 0.5 2.9 O 40.1
C: .63 C: .63

 

 

 

a. Total percent reported

Summary of Testing Hypothesis Eleven

The results of the Contingency Coefﬁcient estimation showed that association
patterns of information search and purchase vary by product category (Table 4-
29). Contingency Coefficient statistics were similar for the US and Canada data.

Hypothesis 11 (travel information search behaviors and travel product
purchase behaviors are associated, however, not perfectly) was not fully
accepted. Travel information search and travel product purchase on car rentals
and flights were strongly associated as the C values were close to the largest
possible value (0.87). Travel information search and travel product purchase on

activities and attractions were associated but not strongly associated.

 

 

 

Table 4-28
Summary of Association_s_
Q Canada
Contingency Contingency
Coefﬁcient Coefﬁcient

Car Rentals .64 .63
Flights .63 .63
Accommodation .49 .47
Events .36 .37
Activities .33 .23
Attractions .33 .21

 

118

CHAPTER 5
SUMMARY, DISCUSSION AND lMPLICA'I'lONS
This chapter serves as a synthesis of the hypotheses, literature, methods and
results previously discussed. The chapter is organized into the following topics:
(1) a summary, (2) discussion of ﬁndings, (3) theoretical implications, (4)
managerial implications, (5) future research, (6) limitations of the results, and (7)

ﬁnal comments.

Summary

The problem statement of this research was to understand online travel
planning strategies during the pretrip stage for a general population sample of
Internet users, and to examine which factors signiﬁcantly encourage or
discourage online travel information search and product purchase during pretrip
planning. Speciﬁcally, the study sought to answer the following research
questions: (1) what factors signiﬁcantly affect online travel information search
during pretrip; (2) what factors signiﬁcantly affect online travel product purchase
during pretrip; and (3) how do online travel information search and online travel
product purchase interrelate with each other.

A conceptual model of travel planning and travel behaviors was developed
from a conceptual model of case-based vacation planning by Stewart and Vogt
(1999) (Figure 1-1). Travel behaviors were separated into three stages, pretrip,
trip and post-trip. This research focused on travel information search and

product purchase behaviors during the pretrip stage. To understand different

119

lntemet uses for travel information search and travel product purchase during
pretrip planning, several variables which affect travel information search and
product purchase behaviors during pretrip were identiﬁed from the literature
(Figure 1-2). General background, tn'p speciﬁc behavior during trip, and lntemet
use for the latest trip during pretrip were developed as independent variables. To
understand the interrelationship between travel lnfonnation search and travel
product purchase behaviors during pretrip, association of travel information
search and travel product purchase were examined across six travel products.
Hypotheses were developed from 1a to 11 as a result of combination of this
research questions and the literature review.

The subjects of this study were 2,470 US and Canada residents (54 percent
of responses from US and 46 percent from Canada), whose age is 18 years old
or more. They had web access, were willing to complete the online instrument,
reported web usage for travel or vacations (i.e., planning, researching, reserving,
or paying credit card for travel products) and took at least one vacation in the
past 12 months from November 2000 to October 2001.

This research was based on secondary data analysis using data collected
between November 8'“ and December 18‘“, 2001 by the CTC and SECOR. The
data were separately analyzed by US and Canada residents. To test
Hypotheses 1a to 10c, cross-tabulation and Chi-square were used for examining
statistical signiﬁcance of difference on relationship between independent
variables and travel information search or travel product purchase across six

travel products. Contingency Coefﬁcient analysis was used to test Hypothesis 11

120

to compare the magnitude of association between travel information search and
travel product purchase across six travel products. Cross-tabulation was also
used in testing Hypothesis 11 to provide details of associations in variables.

US respondents were more likely to travel in the past year, spend time online
per week and used a credit card for online purchase than Canada respondents.
Canada respondents were more likely to have higher education levels, use a
high speed of Internet connection and stay longer on the latest trip than US
respondents. US respondents were more likely to travel out of state but within
country and travel during the fall compared to Canada respondents. Canada
respondents were more likely to travel out of North America and travel during the
summer compared to US respondents. Time spent online per week did not
signiﬁcantly differ in regard to travel information search behaviors in the U.S.
However, time spent online for the latest trip signiﬁcantly differed in regard to
information search for all six products in both the U.S. and Canada and
purchases for all six products in the U.S. Speed of lntemet connection did not
signiﬁcantly differ on purchasing behaviors in Canada. Length of stay of the
latest trip signiﬁcantly differed on lnfonnation searching for all six products in
Canada. Other independent variables signiﬁcantly differed in regard to
information search and product purchase for some travel products. Travel
information search and product purchase on car rentals and ﬂights were strongly
associated. Travel lnfonnation search and travel product purchase on activities

and attractions were associated but not strongly associated.

121

Discussion of Findings

One of the ﬁndings of this study was that travel lnfonnation search and travel
product purchase behaviors during pretrip are different. Respondents were more
likely to use the Internet for their travel information search, but they were less
likely to use the lntemet for their travel product purchase during pretrip.
According to Tables 4-5 and 4.6. US respondents searched for information more
online than ofﬂine on all six products. But they purchased only accommodations,
car rentals and ﬂights more online than ofﬂine. Canada respondents also
searched for information more online than ofﬂine on all six products, but they
purchased all six products more ofﬂine than online. This result supports Stewart
and Vogt’s (1999) ﬁndings: the plans developed by planners will be subject to
change as they are actuated; and people plan more than they will actuate. This
study also found that credit card use for general online purchase was more likely
to signiﬁcantly affect travel product purchase than information search behaviors.
This result is somewhat consistent with Weber and Roehl’s (1999) research,
which found that travelers who purchase online have more years of Internet use
experience and more browser use per week than travelers who did not purchase
online.

Another ﬁnding of this study was travel information search and product
purchase behaviors are different by county of residence. US respondents were
more likely to search for information and purchase online and less likely to
search for information and purchase ofﬂine, than Canada respondents. In the

tests of Hypotheses 1a and 4a, country of residence signiﬁcantly differed in

122

 

regard to travel information search on car rentals and ﬂights, and in regard to
travel product purchase on accommodations, attractions, car rentals, events and
ﬂights. Country of residence was controlled during hypothesis testing to compare
US and Canada results. Many results showed inconsistent results between US
and Canada respondents. US respondents were more likely to take vacations,
spend time online per week, purchase online with credit cards, and travel out of
state but within country, than Canada respondents. Canada respondents were
more likely to travel out of North America, stay longer on their latest trip, use a
high-speed lntemet connection, and search for information and purchase ofﬂine,
than US respondents. As Pastore (2000b) suggested, Canada respondents
were more likely to spend greater amounts of time surﬁng the web than US
residents, then use ofﬂine purchase channels. Another interesting ﬁnding was
that although Canada respondents were more likely to use high-speed lntemet,
the speed of lntemet connection only affected US respondents’ lntemet use.

A third ﬁnding was that people use various channels, such as online, ofﬂine
and both onlofﬂine, for travel information search and product purchase. This
research tested different behaviors of online, ofﬂine, onloffline and none across
six travel products, while past studies just tested which demographic proﬁles use
(or not use) the lntemet with no measure of onlofﬂine usage patterns or
differentiation of ofﬂine marketing channels. Examples of these past ﬁndings
include Weber and Roehl’s (1999) study, which reported that online users were
likely to be 26 to 55 years old and more educated. Korgaonkar and Wolin (1999)

found gender not to be signiﬁcantly correlated with online information search, but

123

males were more likely to purchase than women. This study found that US male
respondents were more likely to search for information on and purchase travel
transportation products (i.e., car rentals and ﬂights) than females, and US
females were more likely to search for attraction information online than males.
Additionally, younger respondents (both US and Canada) were more likely to
search for information online and ofﬂine on activities and purchase activities
ofﬂine, than older respondents. US respondents with higher education levels
were more likely to use the Internet for both travel lnfonnation search and travel
product purchase than those with less education. Results for Canada
respondents showed that those with lower levels of education were more likely to
search for information online on attractions and purchase car rentals online than
respondents with higher education levels. Therefore, while past research has
focused on “who” was more likely to use the lntemet, this thesis study showed
“who" uses the lntemet and “how” it is used for information search and
purchasing, across the primary travel product categories.

A fourth ﬁnding was travel lnfonnation search and product purchase
behaviors were more likely to relate to a speciﬁc purpose of lntemet use.
Pastore (1999) suggested that lntemet users have developed their own online
strategy for each purpose of lntemet use. Online information search and
purchase behaviors are different by lntemet users' different purpose(s). As
shown in results of Hypotheses 3b, 7b and 8b, time spent online for general
lntemet use did not signiﬁcantly differ in regard to travel information search and

product purchase in the U.S.; however, time spent online for a speciﬁc purpose

124

 

of lntemet use (lntemet use for the latest trip) signiﬁcantly differed in regard to
travel information search and product purchase in the U.S. This result also
supports Stewart and Vogt’s (1999) research which found that experience
teaches people how to plan.

The ﬁnal ﬁnding of this study was that patterns of travel information search
and product purchase vary by travel product categories. According to
Hypothesis tests 1a to 100, searching for lnfonnation on or purchasing ﬂights and
car rentals were most likely to be affected by independent variables. The results
of the Contingency Coefﬁcient estimation also varied by travel products (Table 4-
34). The highest association was found for car rental information search and
purchase, followed by ﬂights and accommodations. The lowest association was
found for attraction information search and purchase, followed by activities and
events. This result supports past research. For instance, Fodness and Murray
(1999) suggested features of each travel product clearly affect information
search; Stewart and Vogt (1999) found that the amount of information searched
for and purchasing patterns differed by travel products; and Woodside and
McDonald (1993) presented a general system framework for understanding

different tourists’ choice by travel products.

Theoretical Implications
Stewart and Vogt (1999) found three unique characteristics of planning from
case-based vacation planning: the plans developed by all planners will be subject

to change as they are actuated; people plan more than they will actuate, as they

125

 

compensate for congestion and uncertainty by overplanning; and experience
teaches people how to plan. Case-based vacation planning theory is well
supported in the lntemet environment. lntemet enhances the information search
process and encourages consumers to purchase travel products prior to leaving
for their vacation. Case-based vacation planning suggests that travel behaviors
are separated by three stages, pretrip, trip and post-trip, and travel products were
differently searched for and purchased. This research, based on online travelers'

behaviors, shows consistent results with case-based vacation planning.

Managerial Implications

This thesis found people use the lntemet differently for travel information
search and product purchase during pretrip. Travel marketers are required to
understand different lntemet uses as “a functional information source” and
“shopping outlet” for marketing. Travel websites need to be designed to guide
effective and efﬁcient travel information search and travel product purchase.

Professionals have focused on understanding online travelers' socio-
demographic proﬁles for their marketing management. According to this study,
males were likely to search for lnfonnation and purchase transportation products,
females were likely to search for information on attractions, younger people were
likely to search for information online and ofﬂine on and purchase ofﬂine activities,
and Canada respondents with lower education were likely to search for

information online on attractions and purchase car rentals online. These results

126

suggest demographic differences exist for travel Internet usage and marketing
managers should design lnfonnation systems to facilitate those differences.

During pretrip, more information search occurs than purchase. Many online
information searchers switch to ofﬂine for their purchase. This study also found
that people who used the lntemet longer were more likely to use online and
ofﬂine together. Marketers need to continue collaborating with ofﬂine travel
product providers. Moreover, marketers need to develop new target marketing
strategies for information searchers who switch to ofﬂine for purchase, use both
online and ofﬂine for purchase, or drop out. Different marketing promotions and
pricing structures (e.g., discounts) may attract them to use online for their
purchase.

People were shown to purchase accommodations, car rentals and ﬂights
online. These three products have been provided through by private companies
which are leading online travel-related businesses. Through the lntemet, these
companies were able to reduce cost of promotion, commission to travel agencies,
paper tickets, personnel for management and so on. Moreover, the lntemet has
enhanced direct and fast communication with customers for better services. This
research found that many people also searched for information on attractions
and activities. This research suggests that activities, attractions and events
should be provided through the lntemet as new online markets. According to
Stewart and Vogt’s study (1999), these products are the most changeable

products to purchase during trip. In the future, mobile technology will be

127

generally used to provide these changeable products while people are on their
trip.

Canada residents were more likely to search for information and they were
more likely to purchase ofﬂine. Michalak and Jones (2003) found that Canadian
retailers hesitate to develop e-commerce business because IT business requires
a high-risk, innovative, fast-moving culture that simply does not exist in Canada's
business culture. Because of lack of opportunities to use Canada websites,
Canada consumers visit US websites. However, for the purchase of travel
products, US websites may be inconvenient for Canada residents because of the
currency exchange rate and delivery system (e.g., mailing systems through post
ofﬁce takes longer because it is international mail). This may explain why
Canada residents were more likely to search for travel information online, but
purchase ofﬂine. US travel websites could provide special deals to Canada
residents, such as faster delivery or currency exchange beneﬁts to encourage
Canada residents to purchase online. Travel-related businesses in Canada need
to be engaged in e-commerce and develop more travel websites to attract

Canada residents, and possibly US residents.

Future Research
lntemet use for travel planning is a reﬂection of general lntemet use, as well
as an extension of traditional travel planning and purchasing behaviors. Travel
studies have accepted research methods from general lntemet use studies to

understand online travel behaviors. Most previous research studies have

128

focused on providing demographic proﬁle information on lntemet travelers
(Morrison et al., 2001). This research examined variables from general lntemet
use studies, and also new variables (e.g., country of residence, length of stay of
the latest trip, destination of the latest trip, season of the latest trip) from
traditional travel planning and purchasing behavior studies based on literature
reviews. It is recommended that these relationships be retested with different
samples. In addition, more comprehensive and creative scientiﬁc research is
needed to better comprehend travel e-commerce trends (Morrison et al., 2001).

This research focused on those independent variables that signiﬁcantly differ
on travel lnfonnation search and travel product purchase behaviors. However,
this study could not explain cause-effect relationship between independent
variables and travel information search or travel product purchase. For example,
this research found that US respondents’ length of stay of the latest trip
signiﬁcantly differed in regard to travel lnfonnation search behaviors. But this
result has two possible explanations; US respondents searched for more
information and found more interesting attractions and activities, so they decided
to stay longer; or, US respondents were thinking to stay longer, so searched for
more information. Future research needs to study these cause—effect behaviors
to better understand online travelers’ behaviors.

Travel information search and product purchase behaviors are different
between US and Canada respondents. National cultural differences could be
reasons for the differences. However, another reason is possible for the

differences. In the lntemet environment, even a two-year-gap causes a big

129

difference in web use behaviors. Amazon.com is famous as a “ﬁrst mover.” It
ﬁrst launched an lntemet bookstore, and this ﬁrst mover advantage enabled
Amazon.com to build a brand name and loyalty that could not have been
reversed even with the massive advertising and marketing campaign of Barnes &
Noble (Michalak & Jones, 2003). According to Michalak and Jones (2003),
Canada lags roughly three years behind the US in delivering business solutions
over the lntemet. Thus, three-year-gap may have caused signiﬁcant differences
in the lntemet use for travel planning between US and Canada. Future travel
lntemet research needs to consider that a technological gap, speciﬁcally related
to the lntemet, could cause different patterns on travel information search and

travel product purchase.

Limitations of the Results

There are some limitations of the results:

1. The original data this research used were collected through online surveys.
Therefore, the sample is not representative of the general population. As shown
in a description of the respondent demographic proﬁle in chapter 4, younger and
older age groups were underrepresented compared to national population data.

2. Due to the limitations of the secondary data used in this study, the
methodologies that could be applied for further and more in-depth analysis were
limited (So & Morrison, 2003).

3. Associations of travel lnfonnation search and travel product purchase

behaviors during pretrip were shown in Tables 4-23 to 4-28. Since this study

130

focused on pretrip behaviors, a response of “none.” It could be interpreted in
several ways.

4. Chi-square tests used in this thesis research for testing Hypotheses 1a to
10c showed signiﬁcant differences between independent variables and
dependent variables. In some cases, even though an independent variable
signiﬁcantly differed travel information search or travel product purchase, a

speciﬁc pattern was not found.

Final Comments

This research was seeking to identify differences and interrelationships
between travel lnfonnation search and product purchase behaviors during
pretrip. The problem statement of this research was to understand online travel
planning strategies during pretrip stage for a general population sample of
Internet users, and to examine which factors signiﬁcantly encourage or
discourage online travel information search and product purchase during pretrip
planning. The purpose of this analysis was to clarify similarities and differences
in trip planning and travel behaviors between online and ofﬂine information
searchers and purchasers so that destination marketing organizations can better
respond to consumer’s travel planning behaviors. This study was aimed to help
tourism marketers develop better strategies for providing lnfonnation desired by
potential travelers and efﬁcient information search and purchase channels. It has
become clear that consumers separately treat the lntemet as “a functional

information source” and “shopping outlet.” Consumers were likely to have

131

different lntemet use strategies for each purpose of lntemet use (e.g., for spring
break vacation trip, for banking, for trading stock and so on). The lntemet was
used differently for travel information search and travel product purchase. To
understand different uses of the lntemet for travel lnfonnation search and travel
product purchase, several unique features of this study were attempted, that is:
the study focused on pretrip behaviors; six travel products were separately
analyzed; country of residence was controlled to compare the results between
US and Canada respondents; and, new elements related to trip speciﬁc
behaviors were added as independent variables. It is hoped that these new

attempts will stimulate travel research on travel-related lntemet use studies.

132

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APPENDIX

140

Appendix 1

Selected Survey Questions and Its Measurement Scales

Original Survey Question

Original

Measurement Scale

Modiﬁed
Measurement Scale

General Background:
Tourists’ Demographics

Country of Q1 . Please enter either your ZIP Open-ended Nominal
residence code, or the ﬁrst three digits of -USA
your POSTAL code -Canada

Gender Q2. Gender Nominal no change

-Male
V .. _ .. 77777 g-Femalew ‘

Age Q3. What is your age? Ordinal Ordinal
-18 to 24 -18 to 24
-25 to 29 -25 to 34
-30 to 34 -35 to 44
-35 to 39 ~45 to 54
-40 to 44 -Older than 54
-45 to 49
~50 to 54
-55 to 59
-60 to 64
-65 to 70
-71 or older

Education Q9. What is the highest level of Nominal Nominal

education you have completed? -Less than high -Less than a college
school degree
-Some high school -College degree or
-Graduated high more
school
-Some university/
college! technical
school
-Graduated
university! college/
7 . technical school
. Travel Experience . _g g g . .

Number of Q31. How many vacation, leisure Ordinal no change

vacation trips or get-away trips have you taken -1, 2, 3, 4, and

in recent 12 in the past 12 months? 5 or more

months

 

141

Appendix 1

Selected Survey Questions a_nd its Measurement Scales (continued)

Original Survey Question

Original
Measurement Scale

Modiﬁed
Measurement Scale

 

lntemet Use
Years of
lntemet use

Time spent
online per
week

Speed of
lntemet
connections

Credit card
use for online
purchase

Q24. How long have you been

using the lntemet to surf/browse

the World Wide Web?

Q23. How many hours, in total, do

you personally surf/browse the
lntemet for work or personal
reasons in an average week?

022. What is the connection

speed of the computer you used

the most for planning your last

leisure trip on the lntemet? (select

one)

027. Have you purchased
anything on the lntemet with a
credit card? (select one)

Ordinal

-Less than 6 months
-6 months to 1 year
-1 to 2 years

-2 to 4 years

-4 to 6 years

-More than 6 years
Ordinal

-2 hours or less

-3 to 4 hours

-5 to 10 hours

-11 to 20 hours

-21 to 30 hours
-More than 30 hours

Nominal

-High speed (e.g.
T1, Cable, ISDN.
ADSL)

-Regular speed (e.g.
28-56K)

Nominal

-Yes

-No

Ordinal

-Less than 2 years
-2 to 4 years

-4 to 6 years
-More than 6 years

Ordinal

-Less than 5 hours
-5 to 10 hours

-11 to 20 hours
-More than 20 hours

no change

no change

Travel Planning Behavior During Pretrip:

Internet Use for the Latest Trip
Q38. How long before taking this
trip to (destination) did you begin

Planning
horizon for
Internet use
for the latest
trip

Time spent
online for the
latest trip

planning/researching it on the

lntemet? (select one)

039. How many hours in total, did

you spent on the lntemet

planning/researching your trip to

(destination)? (select one)

Ordinal

-Same day

-The same week
-1 to 2 weeks

-2 to 4 weeks

-1 to 2 months

-2 to 4 months

-4 to 6 months

-6 to 12 months
-More than 1 year

Ordinal

-Less than ‘/2 hour
-‘/2 hour to 1 hour

-1 to 2 hours

-3 to 5 hours

-6 to 10 hours
-More than 10 hours

Ordinal

-Less than 2 weeks
-2 to 4 weeks

-1 to 2 months

-2 to 4 months
-More than 4
months

Ordinal

-2 hours or less
-3 to 5 hours

-6 hours or more

 

142

Appendix 1

Selected Survey Questions and its Measurement Scales (contlnued)

Original Survey Question Original

Modiﬁed

Measurement Scale Measurement Scale

 

‘ Travel lnfonnation Search for the Latest Trip

Accommodations
Activities
Attractions

Car rental
Events

Flights

Accommodations
Activities
Attractions
Car rental
Events
Flights

049. Before you traveled to Nominal
(destination), where did you -Online
most Ieam or research about the -Offline
following? (select one) (NlA -Both
means Not Applicable) -N/A

-Accommodations (description,
availability, prices, facilities)
-Activities (hiking, bird watching,
rock climbing, etc.)
-Attractionslsight-seeing
(monuments, museums, etc.)
-Car rental (prices, availability)
-Events (attending sporting
events, theatre, festivals,
concerts, etc.)

-Flightlairfares (schedules,
avaIlabIlIty)

054. Prior to leaving on yoUr trip "Mammal"

to (destination), please select -Online
those items that you purchased -Ofﬂine
and the method in which they -Both
were purchased -N/A
(select a response for each

item)

-Accommodationlover-night
lodging (hotel, motel, hostel,
B&B, camping etc)

-Activities available atlaround
destination (scuba. horseback
riding, ski lift/pkgs, etc)
-Attractions/sight-seeing
(monuments, museums, etc)
-Car rental

-Events (sporting, theatre,
festival, concerts, etc)
ﬂights/airfare

143

no change

no shange

Appendix 1
Selected Survey Questions and Its Measurement Scales (continued)

Original Modiﬁed

0"9'"a' Survey Question Measurement Scale Measurement Scale

 

Trip Specific Behavior During Trip:

Latest Trip Behavior ,
Length of stay 037. Number of nights away Ordinal Ordinal
of the latest trip from home? -1, 2, 3, 4, 5, 6, 7, -Weekendlshort (1

-October 2001

8,9,10,11,12,13, to 2 nights)
14,15,16,17, 18, -Up to 1 week (3 to
19, and 6 nights)
20 or more -2 weeks (7 to 13
nights)
, 7 7 . 7 7 . . -14 nights or more
Destination of 035. Where is this destination? Nominal no change
the latest trip -Within
province/state
-Out of
province/state,
within country
-Out of country,
within North
America
-Out of North
Season of the 036. When did this trip start? Ordinal Nominal
latest trip (November 2000 to October -November 2000 -Winter (Dec. to
2001) -December 2000 Feb.)
-January 2001 -Spring (Mar. to
-February 2001 May.)
-March 2001 -Summer (Jun. to
-April 2001 Aug.)
-May 2001 -Fall (Sep. to Nov.)
-June 2001
-July 2001
-August 2001
-September 2001

 

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