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This is to certify that the
dissertation entitled

A STUDY OF CHANGES IN PATTERNS OF TRAVEL
‘ BEHAVIOR OVER TIME:
A COHORT ANALYSIS APPROACH

presented by
Chang Huh

has been accepted towards fulﬁllment
of the requirements for the

Park, Recreation and
PhD. degree in Tourism Resources

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2/05 p:/C|RC/Dale0ue.indd-p.1

A STUDY OF CHANGES IN PATTERNS OF TRAVEL BEHAVIOR OVER TIME:
A COHORT ANALYSIS APPROACH

By

Chang Huh

A DISSERTATION

Submitted to
Michigan Sate University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Park, Recreation and Tourism Resources

2006

ABSTRACT

A STUDY OF CHANGES IN PATTERNS OF TRAVEL BEHAVIOR OVER TIME:
A COHORT ANALYSIS APPROACH

By
Chang Huh

One of the many challenges facing the American tourism industry in the 21St
century is rapid changes in the demographic characteristics of the US. population. The
latest 2000 Census indicates that the US. population has reached 281.4 million, a 13.2
percent increase over the 248.7 million people recorded in the 1990 Census, and that
signiﬁcant shifts in the population structure occurred over the decade between the
censuses. Another challenge facing the US tourism industry is accounting for unique
events or environmental changes that occur while people are planning or taking a trip
which inﬂuences their travel behavior.

The purpose of this study was to investigate the causal relationship between
changes in patterns of travel behavior over time with respect to the effects of biological
age, time of travel, and generational cohort. In the study, biological age captures changes
in patterns of travel behavior produced by physical or life cycle changes at different
points in time. Time of travel captures changes in patterns of travel behavior inﬂuenced
by unique events or environmental changes that occur during a period of observation.
Generational cohort captures changes in patterns of travel behavior among those born
during the same time interval.

Household data from Illinois, Indiana, Michigan, Ohio, Wisconsin, and the
Canadian province of Ontario were utilized for this study. Households (n=5,735) who

had taken a pleasure trip during the past 12 months to any destination in 1997 and 2002

were used for the analysis. Independent samples Hosts and One-way Analysis of
Variance tested the relationship between selected travel behaviors and the effects of
biological age, time of travel, and generational cohort over time, and then multiple
regression and logistic regression were employed to identify key determinants that
explain additional variation in selected travel behaviors.

The results of the study provided evidence of biological age, time of travel, and
generational cohort impacts on selected travel behaviors over time. Speciﬁcally,
travelers’ trip planning horizon has become shorter during the ﬁve-year period. Those
traveling in 2002 had 2.7% shorter trip-planning intervals than those traveling in 1997;
and those born between 1970 to 1974 had 20.3% shorter trip-planning intervals than
those born between 1980—1984. The odds of a respondent searching for travel
information online were 236.7% higher for 2002 pleasure travelers than for 1997 pleasure
travelers; 40.5% higher for those who were born in 1975—1979 than for those born in
1980-1984; and 42.3% lower for those were born in 1935-1939 than for those born in
1980-1984.

The timing of intensive tourism advertising and promotion should be considered
before tourism season begins due to people’s short trip planning horizon. Generational
cohort can be used as generational marketing and Internet market segmentation strategies
to differentiate, customize, and personalize online tourism products and services. Future
research should extend this study to different travel behavioral variables in order to
identify new or different tourism target markets. To reveal travel behavior changes over

time, longitudinal market research is strongly recommended.

Copyright by
Chang Huh
2006

Dedicated to my father, Cho Huh, my mother, Okhwan Oh, my mother-in-law,
Jongsoo Kim, and my wife, Seunghye Son for all their love, patience, support, and
encouragement along the way.

ACKNOLEDGEMENTS

Time ﬂies like an arrow. Since I came to pursue a Ph.D. degree in the United
States, I have worked with and learned from many collegues, scholars, and friends who
have made my life, teaching, and research meaningful, rich, and valuable. They made it
possible for me to travel down this long and wild road. I would like to take this
opportunity to express my appreciation to all of those wonderful people, although it will
not be enough to express my gratitude in words to them.

Dr. Donald Holecek, chairman of my Ph.D. guidance committee and my adamic
advisor, must be recognized for the ﬁnancial asistance and academic guidance he
provided throughout my Ph.D. program. His invaluable comments and constructive
criticism were essential to my success and are greatly appreciated. His strong editoral
hand helped mold this manuscript in countless ways. I am also greatly indebted to PhD.
guidance committee members and co-researchers: Dr. Christin Vogt, for her friendship,
numerous helpful comments, and willinglness to listen; Dr. Hairong Li, for his valuable
insight and encouragement; and Dr. Arjun Singh, for his ﬁ'iendship and encouragement.
Special thanks must go to Professor Gaylan Rasmussen, Dr. Scott Witter (chairperson of
Community, Agriculture, Recreation and Resource Studies), Dr. Daniel Spencer (Black
Hills State University), and Dr. Betty van der Smissen (University of Northern Iowa) for
patiently listening to my ideas and giving me the conﬁdence and support to continue. I
would also like to thank all stafff members of the Michigan Travel, Tourism, and
Recreation Resource Cener for their assistance and fellowship; the Department of Park,
Recreation and Tourism Resources for its assistance; and the Graduate School for its

ﬁnanical support and assistance.

vi

I could not have written this manuscript without the support of faculty and staff
members at Arkansas Tech University. Dr. Theresa Herrick, chairperson of Parks,
Recreation and Hospiatality Administration, took the time to offer invaluable suggestions
and encouragement as well as eased my workload to get this manuscript done. Dr. Glen
Bishop, alumnus of Michigan State Unversity,, provided advice, support, and endless
encouragement at the times these were most needed. Dr. Brenda Montgomery, Professor
Ray Moll, Professor Cathi McMahan, Ms. Cynthia Hovis, Ms. Cindy Condley, Dr. Marry
Gunter, and Dr. Sandy Chen provided encouragement, a comfortable zone, and a sense of
humor to enable me to take a breath when I felt overwhelmed from teaching, researching,
and writing this manuscript.

Of course, it would have been impossible to complete my degree without the
support of people who convinced me to become an international scholar in my home
country. Dr. Hong-bumm Kim, my academic advisor and mentor during my MS. degree
at Sejong University, took time from his extremely busy schedule to talk with, e-mail,
and encourage me. In addition, Dr. Kim’s research lab collegues, Kyusung Hwang,
Taehwan Kim, Kyunghwan Nam, Youngsoo Kim, and Hyun-a Kim, have provided
encouragement, support, inspiration, and lifelong friendship. Dr. Hee Chan Lee, alumnus
of Michigan State Unversity, helped me open my eyes to the knowledge of research
while giving me his ﬁrm friendship. Myungyeob Park, research fellow and alumnus of
Kyonggi University, provided deep friendship and countinous encouragement. I would
also like to thank my spiritual Japanese parents, the deceased Haruki Shinto and Hisako
Shinto, as well as my revered Japanese teacher, Tsuyako Takeshita, for their love, endless

encouragement, and prayers.

vii

I am also indebted to many other friends and collegues at Michigan State
Unversity: the Absolutees’ members (the Social Science Research Club) and family,
Seunghyun Kim, Jaemin Cha, Jeonghee Noh, J inyoung Choi, Hyunju Choi, So-Yeon Ann,
Mikyung Kim, Seoki Lee, Jinwoo Moon, and Mikyung Kim, who have all given their
supporative fellowship and encouragement. Songjae Jo and family and Sandjun Lee and
family have always given their friendship and prayers. My roommates Youngrae Kim,
Seajun Ahn, and Jaehoon Choi have consistently supported me through their sincere
friendship and encouragement. Sung Hee Park, Azlizam Aziz, Asli Tasci, Hung-Hsu Yen
and family, Charles Jin Yan Shih and family, Ya-Yen Sun, Ariel Rodriguez, Carla
Barbieri, Adi Susilo, and Krishna Shrestha gave their sincere fellowship. The Banzai
English Club teachers, Maxine Klan and family, and Mark Cross and family have given
their love, encouragement, and prayers.

I wish to extend my gratitude to my parents, my brother and sisters, my mother-
in-law, and my brothers- and sisters-in-law for their love, support, and encouragement.
Finally, I will never forget the incredible selﬂessness of my wife, Seunghye Son, who
endured so much to make it possible for me to work for long stretches without feeling
guilty about neglecting my only baby girl, Yujin. At last, I can spend more time with my
wife and baby.

I am now moving on the next stage of my life. I know that being a good teacher
and researcher is no simple matter, but I don’t mind applying my constant efforts to
achieve this goal along my life path. Although I could not recognize all the wonderful
people who have helped me complete this work, I will never forget their help and prayers

for my success. I will repay their love and high spirits to those whom I meet in the future.

viii

 

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................... XII
LIST OF FIGURES ........................................................................................................ XIII
CHAPTER I
INTRODUCTION ........................................................................................................... 1
Background .................................................................................................................. 1
Problem Statement ....................................................................................................... 3
Study Purpose .............................................................................................................. 5
Research Questions ...................................................................................................... 7
Study Objectives .......................................................................................................... 7
Study Hypotheses ........................................................................................................ 8
Delimitations ............................................................................................................... 9
Deﬁnitions ................................................................................................................... 9
Signiﬁcance of the Study ........................................................................................... 11
Organization of the Study .......................................................................................... 12
CHAPTER II
REVIEW OF THE LITERATURE ............................................................................... 14
Biological Age ........................................................................................................... 14
The Time of Travel .................................................................................................... 18
The Year 1997 ....................................................................................................... 18
Economic Environment in 1997. ....................................................................... 18
Gasoline Prices .................................................................................................. 19
Computer and Internet Penetrations .................................................................. 20
The Year 2002 ....................................................................................................... 21
Economic Environment in 2002 ........................................................................ 21
Gasoline Prices .................................................................................................. 21
Computer and Internet Penetration .................................................................... 22
Generational Cohort .................................................................................................. 23
Cohort Analysis as a Research Method ..................................................................... 31
Concepts of Cohort Analysis ................................................................................. 31
Analytical Methods in Cohort Analysis ................................................................ 32
Standard Cohort Table Method ......................................................................... 33
Triad Method ..................................................................................................... 34
Analysis of Variance Method ............................................................................ 36
Constrained Multiple Classiﬁcation Method ..................................................... 36
Cohort Studies in Tourism Research ......................................................................... 38
Modeling Studies of Travel Behavior in Tourism Research ..................................... 43
Travel Propensity Model ....................................................................................... 43
Trip Flaming Horizon and Information Search Online Models ........................... 45
Travel Expenditure Model ..................................................................................... 47
Conclusions and Implications of Literature Review ................................................. 48

ix

CHAPTER III

METHOD ...................................................................................................................... 50
Data Source ................................................................................................................ 50
Study Population and Sampling ............................................................................ 51
Study Instrument .................................................................................................... 52
Data Collection and Procedures ............................................................................ 53
Response Rate ........................................................................................................ 54
Data Preparation ........................................................................................................ 55
Data Selection Procedures ..................................................................................... 55
Data Weighting ...................................................................................................... 56
Modiﬁcation of Variables ...................................................................................... 57
Data Analytical Methods and Procedures ................................................................. 60
Frequency and Descriptive Analyses .................................................................... 60
Sociodemographic Proﬁle of Pleasure Travelers .............................................. 61
Pleasure Trip Characteristics ............................................................................. 63
Independent Samples t-test .................................................................................... 67
One-way Analysis of Variance .............................................................................. 68
Multiple Regression ............................................................................................... 69
Logistic Regression ............................................................................................... 71
CHAPTER IV
RESULTS AND DISCUSSION .................................................................................... 73
Three Dominant Study Variable Effects on Selected Travel Behavior Variables ....74
The Effect of Biological Age on Selected Travel Behavior Variables .................. 74
Travel Propensity ............................................................................................... 76
Trip Planning Horizon ....................................................................................... 76
Propensity to search for Travel Information Online .......................................... 77
Travel Expenditures ........................................................................................... 79
The Effect of Time of Travel on Selected Travel Behavior Variables .................. 80
Travel Propensity ............................................................................................... 80
Trip Planning Horizon ....................................................................................... 81
Propensity to Search for Travel Information Online ......................................... 81
Travel Expenditures ........................................................................................... 82
The Effect of Generational Cohort on Selected Travel Behavior Variables ......... 83
Travel Propensity ............................................................................................... 83
Trip Planning Horizon ....................................................................................... 85
Propensity to Search for Travel Information Online ......................................... 86
Travel Expenditures ........................................................................................... 87
Summary of Results ................................................................................................... 87
Fitting Selected Independent Variables to the Dependent Variables ........................ 90
Travel Propensity Model ....................................................................................... 90
Trip Planning Horizon Model ................................................................................ 93
Propensity to search for Travel Information Online Model .................................. 99
Travel Expenditures Model ................................................................................. 104
Summary of Results for the Three Independent Variables of Priority Research
Interest ..................................................................................................................... 110

X

CHAPTER V

CONCLUSION ........................................................................................................... 1 13
Summary of Key Results ......................................................................................... 113
Travel Propensity ................................................................................................. 1 13

Trip Plarming Horizon ......................................................................................... 114
Propensity to Search for Travel Information Online ........................................... 115
Travel Expenditures ............................................................................................. 1 17
Implications ............................................................................................................. 1 18
Study Limitations .................................................................................................... 120
Recommendations for Further Research ................................................................. 121
APPENDICES ................................................................................................................. 124
APPENDIX A .............................................................................................................. 125
Core Questionnaire during a Five-year Period: 1997 and 2002 .............................. 125
APPENDIX B .............................................................................................................. 135

Type, Operational Deﬁnition, Original Coding, and Modiﬁcation of Variables 135

BIBLIOGRAPHY ........................................................................................................... 139

xi

LIST OF TABLES

Table 1. US. Economic Indicators in 1997 ....................................................................... 18
Table 2. The US. Economic Indicators in 2002 ............................................................... 21
Table 3. Comparison of Generation and Generational Cohort Classiﬁcations ................. 30
Table 4. Sample of Standard Cohort Table for Travel Propensity .................................... 34
Table 5. Sociodemographic Proﬁle of Pleasure Travelers ................................................ 62
Table 6. Pleasure Trip Characteristics ............................................................................... 64

Table 7. Relationship between Biological Age and Selected Travel Behavior Variables 75
Table 8. The Relationship Between Time of Travel and Selected Travel Behaviors ....... 80

Table 9. Relationship between Generational Cohort and Selected Travel Behavior
Variables ............................................................................................................. 84

Table 10. Statistical Signiﬁcance of the Relationship Between Selected Travel Behaviors
and Three Effects ............................................................................................... 88

Table 11. Stepwise Multiple Regression Results from Fitting Selected Sociodemographic
Variables to Travel Propensity .......................................................................... 91

Table 12. Stepwise Multiple Regression Results from Fitting Independent Variables
(Biological Age, Time of Travel, Generational Cohort, Selected
Sociodemographic Variables, and Selected Trip-Related Variables) to the
Dependent Variable (Trip Flaming Horizon) ................................................... 94

Table 13. Stepwise Logistic Regression Results from Fitting Independent Variables
(Biological Age, Time of Travel, Generational Cohort, and Selected
Sociodemographic Variables) to the Dependent Variable (Propensity to search
for Travel Information Online) ........................................................................ 100

Table 14. Stepwise Multiple Regression Results from Fitting Independent Variables
(Biological Age, Generational Cohort, Selected Sociodemographic Variables,
and Selected Trip-Related Variables) to the Dependent Variable
(Travel Expenditures) ...................................................................................... 105

Table 15. Summary of Results for Selected Signiﬁcant Effects of Biological Age, Time
of Travel, and Generational Cohort on Dependent Variables ......................... ll 1

xii

 

LIST OF FIGURES

Figure 1. Overview of the Study Framework .................................................................... 13
Figure 2. The Changes in Gasoline Prices in 1997 ............................................................ 20
Figure 3. The Changes in Gasoline Prices in 2002 ............................................................ 22

xiii

CHAPTER I
INTRODUCTION
Background

One of the many challenges facing the American tourism industry in the 21“
century is rapid changes in the demographic characteristics of the US. population. The
latest 2000 Census indicates that the US. population has reached 281.4 million, a 13.2
percent increase over the 248.7 million people recorded in the 1990 Census, and that
signiﬁcant shifts in the population structure occurred over the decade between the
censuses. According to the censuses, the American population is aging as Baby Boomers
(those born in the post-World War 11 period between 1946—1964 or those who were
between the ages of 40 and 58 in 2000) advance into middle age. People born during the
early years of the baby boom (1946 through 1950) accounted for a 55 percent increase in
the number of 50 to 54 year olds, the largest percentage increase between 1990 and 2000
of any ﬁve-year age group. The second fastest-growing group was 45 to 49 year olds,
who increased 45 percent in size. The ﬁrst cohort of 77 million baby boomers will reach
the age of 62 in 2008. In addition, the population over age 65 rose by 12 percent from
31.2 million in 1990 to 34.9 million in 2000. Low birth rate, increased life expectancy,
and the aging of the baby boomers are believed to be causal factors in the aging of
America (Godbey, 1997; Longino, 1994). As the American population continues to age,
people in the tourism industry are questioning whether the future older cohort will behave
more like the current population of the same age cohort or more like when they were

younger (Sakai, Brown, & Mak, 2000; You & O'Leary, 2000).

 

Another challenge facing the US tourism industry is accounting for unique
events/incidents or environmental changes that occur while people are planning or taking
a trip, which inﬂuence their travel behavior. An example of such a unique event is the
ﬂuctuation of gasoline prices; since the beginning of 2000, the price of gasoline
nationwide has ﬂuctuated from an average of $1.38 per gallon in January to $1.95 in July
and $1.60 in November (Moss, Ryan, & Wagoner, 2003). In a nationwide survey by the
National Restaurant Association (1995), 62 percent of consumers indicated that they
intend to reduce spending in some areas of their consumption as a result of the higher
gasoline and energy prices (Papiemik, 2001). The higher gasoline price is strongly
believed to play a primary effect in inﬂuencing the trip planning of people (Holecek,
2001) as well as their travel intentions (Cook, 1982). Another example of such unique
events is the terrorist attacks in the United States on September 11, 2001, which changed
many aspects of our lives and the operating environments of many industries, including
the tourism industry. Many changes in travel behavior ensued and many new safety and
security challenges now confront the tourism industry. The threat of terrorism to major
tourist attractions will be an ongoing problem in the United States. In addition, economic
conditions of the nation, such as GDP, employment rate, and technology development,
such as the high-speed Internet and Global Positioning Systems (GPS) are also believed
to affect travel behavior.

In sum, the population’s changing age structure, unique events and environmental
changes have affected the US travel behavior over time. However, little attention has
been paid to the following questions to date: What effect will the aging of the US.

population have on the US tourism industry over time? Have patterns of travel behavior,

such as travel propensity, trip planning horizon, propensity to search for travel
information online, and travel expenditures on trips, been changing as successive young
adult cohorts (e.g., 18 to 24 years-old age group) replace the previous cohort and the baby
boomers reach middle age? And, do unique events, economic conditions, or technology
developments (e.g., computer penetrations, internet usage), have an inﬂuence on people’s
travel behavior while people are planning or taking a pleasure trip? What key factors can

predict travel behavior over time?

Problem Statement

Travel behavior over time seems to be inﬂuenced not only by people’s
chronological/social age (i.e., life cycle status) but also by the era when they emerged
from their youth and became young adults and/or from being young adults to becoming
seniors (Meredith, Schewe, & Karlovich, 2002). Examples of early inquiries into this
issue in the tourism literature include Oppermann (1995), who examined changes in
German traveler patterns and. destination choice with respect to biological age and
generational cohort. His ﬁndings indicated that biological age causes travelers to reduce
distances traveled. However, this ﬁnding is somewhat contradictory to the ﬁnding of
Reece (2004), who investigated the travel behavior patterns of senior travelers (deﬁned as
55 years of age and older) and non-senior travelers in South Carolina, and indicated that
senior travelers travel farther than do their counterparts. Collins and Tisdell (2002) also
investigated linkage between biological age and travel behavior in age-related Australian

outbound tourism cycles by trip purposes. They found that the 15 to 24 years-old age

group was more likely to travel for educational purposes, whereas the 65 and older age
group was less likely to travel for such purposes.

Travel behavior is also affected by unique events/incidents or environmental
changes that arise while people are planning or taking a trip. Corsi and Harvey (1979)
investigated whether the patterns of long-distance vacation travel were impacted by
higher gasoline prices under the conditions of an energy crisis. They found that people
were affected by higher gasoline prices or restricted fuel availability when they planned a
vacation trip. This ﬁnding is consistent with studies of Williams, Burke, and Dalton
(1979) and Kamp, Crompton and Hensarling (1979), indicating that the impact of the
increased prices of gasoline and the expected shortage of gasoline diminished vacation
travel. Furthermore, higher gasoline prices compel people to take shorter trips, and, if
gasoline prices reach an unacceptable level, people will stay home. Morgan (1986) also
examined the impact of energy crises during 1973—1974 and 1979. His ﬁndings revealed
that there was an energy crisis impact that was signiﬁcant for all US. national park visits
and that visits to the Grand Canyon dropped by 25% during the two crisis periods of
1973—1974 and 1979. Furthermore, since the evolution of the Internet in the 19905,
people have utilized the Internet for searching for information and purchasing products or
services online. Such environmental changes have also been found to inﬂuence travel
behavior over time (Beldona, 2005).

Travel is an experience-based activity; thus, the travel experiences in college
students who were born between 1984 to 1994 continue to affect their travel behavior
throughout their life span. Their core values (e.g., attitudes, preferences, and behavior)

are carried through life largely unchanged (Meredith et al., 2002). In other words,

generational cohort members are believed to exhibit similar behavior patterns throughout
their life span due to similar travel experiences in their youth and perceived values within
the cohort (Glenn, 1977; Oppermann, 1995; Zimmer, Brayley, & Searle, 1995). Several
empirical studies support this assumption. Oppermann (1995) found that members of the
younger generational cohort take more trips and travel farther than the previous
generational cohort. You and O’Leary (2000) added to Oppermann’s ﬁnding by
observing Japanese senior outbound travel propensity, activity participation at the travel
destination, and travel philosophy in relation to biological age (i.e., age 55—64 year-old
group in both 1986 and 1995) and generational cohort (i.e., those born between 1931 to
1940). Their ﬁndings showed that the generational cohort effect overruled the biological
age effect. The senior Japanese travelers traveled as much as when they were young and
participated in more activities than their counterparts did a decade ago.

To date, despite the important implications of the changes in travel behavior
inﬂuenced by the individual effects of biological age, time of travel, and generational
cohort, very few studies have examined the joint impact of such changes on aggregate

travel behavior over time in the context of tourism.

Study Purpose
The central question addressed in this research was: what effect will the aging of
the US. population have on the tourism industry over time? This question is of great
importance to the tourism industry because changes in age distribution will inﬂuence
tourism market demand as the US population continues to age. Three independent

variables, which were hypothesized to be most relevant in addressing this question, were

identiﬁed. Biological age was selected because peoples’ interest and physical abilities
change as they become older, and the US. population is becoming older. The year in
which a trip was taken or planned was selected because it is well established that current
events or environmental changes have a signiﬁcant impact on travel behavior. Finally,
Generational cohort was selected because what people experience in their youth
inﬂuences their consumption behavior later in life.

Therefore, the purpose of this study was to investigate the causal relationship
between changes in patterns of travel behavior over time and the following three
variables: biological age, time of travel, and generational cohort. More speciﬁcally, this
study utilized a cohort analytical concept as a methodological framework to identify: (1)
changes that can be attributed to the process of aging (i.e., life cycle status, reduced
physical abilities), (2) changes that are associated with the unique events or
environmental changes (e.g., economic conditions, gasoline prices, technology
development and innovations) at the time trip is taken or trip is planned, and (3) changes
that can be attributed to generational cohort (i.e., those who were born in the same year
have had similar life experiences). The secondary purpose of this study was to further
identify salient factors that inﬂuence travel behavior in relation to the effects of
biological age, time of travel, and generational cohort, as well as sociodemographic

factors and travel-related factors (e.g., travel party size, transportation used).

Research Questions
The following three research questions were central in this study:
. What changes in patterns of travel behavior are associated with the effects of
biological age, time of travel, and generational cohort over time?
. If changes exist, do the effects of biological age, time of travel, or generational cohort
explain differences over time in: (1) travel propensity, (2) trip planning horizon, (3)
propensity to search for travel information online, and (4) travel expenditures on
trips?
. If speciﬁed dominant effects exist, can the effects be combined with other causal
variables (e.g., sociodemographic factors, transportation used) to more fully explain
variations in: (1) travel propensity, (2) trip planning horizon, (3) propensity to search

for travel information online, and (4) travel expenditures on trips?

Study Objectives

This study had the following objectives:

. To document changes in patterns of travel behavior (i.e., travel propensity, trip
planning horizon, propensity to seek travel information online, and travel
expenditures on trips) with respect to the effects of biological age, time of travel,
generational cohort, as well as other key factors that have an impact on travel
behavior.

. To demonstrate the cohort analytical method utilizing longitudinal data to identify

changes in travel behavior.

3. To determine whether statistically signiﬁcant differences in patterns of travel
behavior have occurred across the effects of biological age, time of travel, and
generational cohort with respect to the following travel behaviors: (1) travel
propensity, (2) trip planning horizon, (3) propensity to search for travel information
online, and (4) travel expenditures on trips.

4. To identify causal factors, including biological age, time of travel, and generational
cohort, which can be used to better understand observed variations in travel behavior

in order to enhance targeting of marketing campaigns and forecasting future behavior.

Study Hypotheses

Two research hypotheses were selected to guide this study:

Hypothesis 1: Signiﬁcant differences exist in patterns of travel behavior with
respect to the following activities: (1) travel propensity, (2) trip planning horizon, (3)
propensity to search for travel information online, and (4) travel expenditures on trips that
are associated with differences in: (1) the age of the travelers, (2) current events at the
time the trip is planned, and (3) the generational cohort of the traveler.

Hypothesis 2: The speciﬁed dominant effect(s) can be combined with other causal
variables to more effectively explain variations in the following travel behaviors: (1)
travel propensity, (2) trip planning horizon, (3) propensity to search for travel information

online, and (4) travel expenditures on trips.

Delimitations
The study is delimited to households in the states of Illinois, Indiana, Michigan,
Ohio, Wisconsin, and the Canadian province of Ontario. The data used in the study were
collected during an eight-year period from January 1996 through June 2003 in the study
region. The samples used for analysis in the study were comprised of household members
aged 18 or older between 1997 and 2002 who indicated that they had taken one or more

pleasure trips to any destinations during the past 12 months.

Definitions

For the purposes of this study, it is important to provide clear and speciﬁc
deﬁnitions of the terms used in this study. These include the following six terms:
household, travel or trip, biological age, time of travel, generational cohort, and cohort
analysis.

Household. To accommodate the diversity of household compositions and to
prevent confusion due to various deﬁnitions of household, this study employed the
deﬁnition used in the current population reports of the US. Census Bureau, which deﬁne
“household” as “a social group with whom one resides and occupies a housing unit.”

Travel or trip. “Travel or trip” is deﬁned as “a pleasure trip, including overnight
and day trips, to a place at least 50 miles from home for the purpose of enjoyment.”
Speciﬁcally, pleasure travel includes: vacations, weekend getaways, shopping trips, trips
to a second home, and trips to visit ﬁiends or relatives. In the study travel and trip are

used interchangeably.

Biological age. Biological age refers to the changes in travel behavior produced
by physical or life cycle changes at different points in time (e.g., travel behavior of 18—24
year-olds in 1997 and 2002). In this study, biological age and life cycle status are treated
as the same construct. Since several relevant studies have used “age” term as “biological
age,” the two terms will be used interchangeably.

Time of travel. Time of travel pertains to the changes in travel behavior
inﬂuenced by unique events or environmental changes that occur at a period of the
observation (e.g., the ﬂuctuation of gasoline prices in 2000, 9/11 in 2001, economic
condition, and technology development). Since several previous studies have employed
“period” in place of “time of travel,” the two terms will be used interchangeably.

Generational cohort. Generational cohort refers to changes in travel behavior
among those born during the same time interval. For example, those born between 1959
and 1963 experienced many common signiﬁcant life events, values, and socialization
processes regardless of their age or stage of life. Since several previous studies have
employed “generation,” “cohort,” or “birth cohort” terms as a “generational cohort,” the
four terms will be used interchangeably.

Cohort analysis. Cohort analysis involves examining biological age, time of
travel, and generational cohort effects on changes in the attitudes or behavior patterns of
groups at two or more points in time (Glenn, 1977), as well as determining which of the
three variables accounts for changes over time (Chen, R., Wong, & Lee, 2001; Chen, X.,
Li, Unger, Liu, & Johnson, 2003; Pennington-Gray & Spreng, 2001; Rentz, Reynolds, &

Stout, 1983).

10

Significance of the Study

Most travel behavioral studies have been designed as cross sectional studies
collecting data at one period of time. Thus, their conclusions may be somewhat limited
(Babbie, 2004). There have been very few longitudinal studies of travel behavior. This
study can ﬁll this gap and contribute the following to the tourism literature.

This study will illustrate cohort analysis as a methodological tool to investigate
the changing patterns of travel behavior within a sub-group to be drawn without the need
to query the same individuals at different points in time (i.e., panel study) (Glenn, 1977).
Furthermore, this study will illustrate the many advantages associated with the
application of cohort analysis over the alternative of longitudinal tracking of a sample of
the same individuals over time including: (1) reduced costs, (2) no need to recruit study
participants and retain them for many years (i.e., panel survey), and (3) results can be
obtained using existing data now, thus avoiding the long delay before results can be
obtained from longitudinal tracking surveys of individuals.

By investigating salient factors that explain variations in travel behavior over time
this study will help broaden tourism marketers’ and practitioners’ ability to proﬁle their
target markets and more accurately match their market offerings to individuals and
groups. Finally, knowledge of changing age structure and its effect on patterns of travel
behavior over time will help tourism marketers and promoters identify growth areas,
target segments, or forecast future behavior for, and use of, their products and services, as

well as plan marketing strategies at a macro level.

11

Organization of the Study

This dissertation is comprised of ﬁve chapters. Following this introduction
chapter is a review of literature in chapter 11, which discusses conceptual and theoretical
information relating to the effects of biological age, time of travel, and generation cohort
and reviews the critical studies and methods that have been carried out in this study area.
Modeling studies in travel behavior are reviewed to identify key dimensions that are
inﬂuential on individuals or groups.

Chapter III follows with a comprehensive discussion of the data source used in
this study, which includes: study population and sampling, study instrument, data
collection and procedures, and response rate. A data preparation section describes data
selection procedures, data weighting, and modiﬁcation of variables. An analytical
methods and procedures section presents the types of statistical tools employed and their
use in detail.

Chapter IV contains the results of the analyses examining the inﬂuence of
biological age, time of travel, and generational cohort on selected travel behavior
variables and modeling of these selected independent variables to selected dependent
travel behavior variables.

Finally, chapter V summarizes key results and implications and addresses study
limitations, followed by recommendations for ﬁrrther study. Figure 1 illustrates the ﬂow

of the study.

12

 

 

What effect will the aging of the US.
population have on the tourism industry over

 

 

time?
I

 

 

What changes in the patterns of travel
behavior are associated with biological age,
time of travel, and generational cohort?

 

 

I

 

Review studies and documents relating to
biological age, time of travel, and
generational cohort, as well as key factors
affecting travel behavior.

 

I:

 

 

Do biological age, time of travel, and
generational cohort explain differences in the
patterns of observed travel behavior over
time?

 

 

 

I

 

I

 

 

Test
biological age

 

 

Test
time of travel

 

 

generational cohort

Test

 

 

 

 

 

 

I_'__—l

 

 

I———_I

 

I'_—Ll

 

 

 

n.s. Sig.

 

n.s. Sig.

 

 

 

 

n.s.

Sig.

 

 

 

 

 

 

 

 

 

 

 

 

I
I

 

If dominant effects exist, can the effects be
combined with other causal variables (e.g.,
sociodemographic factors, travel party) to
more fully explain variations in the patterns
of observed travel behavior over time?

 

I

 

 

Identify key factors related to observed travel

 

behavior.
I

 

 

Discuss results of the study and address
implications for the tourism industry.

 

 

Figure 1. Overview of the Study Framework

13

 

Note. “n.s.” indicates no
signiﬁcance.

CHAPTER II
REVIEW OF THE LITERATURE

This study investigated changes in patterns of travel behavior with respect to
study subjects’ biological age, time of travel, and generational cohort. The biological age
variable captures changes in travel behavior due to the physical or life cycle changes that
accompany aging. The time of travel variable captures changes in travel behavior
inﬂuenced by events or conditions at the time that a trip is taken or being planned (e.g.,
gasoline prices, 9/11, economic situation, technology development). The generational
cohort variable captures changes in travel behavior produced by differences that arise
from the life experience, history, values, attitudes, and socialization of cohorts.

This chapter provides the background and theoretical information needed to
understand biological age, time of travel, and generational cohort in a travel context. It
reviews the most important research and methods that have been conducted in this study
area, and establishes this study as one link in a chain of research that advances knowledge
in this ﬁeld. The discussion in this chapter is comprised of (l) biological age, (2) the time
of travel (1997 and 2002), (3) generation cohort, and (4) the methodological
understanding of cohort study and relevant cohort studies, and (5) modeling studies in

tourism research.

Biological Age
Biological age refers to physical or life cycle changes that occur in the aging
process. The importance of biological age has been recognized in the tourism and

hospitality literature. Biological age has been linked to changes in behavior, attitudes, and

14

demands (Schiffrnan & Kanuk, 1997) and is a key predictor of changes in individuals’
behavior and attitudes (Hansman & Schutjens, 1993). An overview of the biological age
stratiﬁcation changes in the US population gives some insights into the phenomenon of
aging.

According to the 2000 Census, the US population increased by 13.2% from 248.7
million in 1990 to 281.4 million in 2000. Signiﬁcant demographic changes in 2000 are
associated with the aging of the US. population, because Baby Boomers (those born
between 1946—1964) have advanced into middle age. The 2000 Census shows that people
born from 1946 through 1950 accounted for a 55% increase in the number of 50 to 54
year olds, the largest percentage increase between 1990 and 2000 of any ﬁve-year age
group. The group of 45 to 49 year olds increased 45%. The ﬁrst cohort of 77 million baby
boomers will reach the age of 62 in 2008.

Furthermore, the age group of 90 to 94 year olds increased 45% as well. The
Census has predicted that the number of people over 65 years of age will double to 80
million by 2050 (McGuire, Boyd, & Tedrick, 2004). Low birth rate, increased life
expectancy, and the aging of the baby boomers seem to be causes of the aging of
America (Godbey, 1997; Longino, 1994). Life expectancy at birth in 1900 was 47.3 years,
but in 1996 it was 76.1 and by 2000 it had risen to 76.9 (McGuire et al., 2004). This
ﬁgure will continue to rise as the level of health improves throughout the later years of
life (Godbey, 1997).

Biological age has been of great interest in the tourism and hospitality literature.
Biological age and travel propensity (i.e., number of trips taken or number of vacation

days away from home) have been found to be correlated. Fleisher and Pizam (2002)

15

studied travel constraints of Israeli seniors (55 or older). They found that biological age
played a signiﬁcant role in the number of vacation days taken each year due to the travel
constraints that come with aging. According to the model proposed in that study, the
number of vacation days taken increased rapidly among people between the ages of 55
and 65 and then gradually decreased thereafter. This ﬁnding is generally consistent with
those of other studies (Mak, Carlile, & Dai, 2005; Mark & Lambert, 2003; Sakai et al.,
2000; Warnick, 1993; You & O'Leary, 2000).

Biological age has also been found to inﬂuence information search behavior.
Gitelson and Crompton (1983) observed that adults aged over 50 were prone to use a
travel agent while planning a trip. Homeman et al. (2002) found that seniors 65 or older
preferred to collect travel information from printed travel guides, pamphlets, or brochures,
from word-of-mouth recommendations, and travel agents. They were least likely to use
the Internet, clubs and associations, and reward programs to obtain travel information.
Since the 1990s the Internet has been as a gold mine of travel information (Connolly,
Olsen, & Moore, 1998). Prior studies provide ample evidence that biological age plays an
important role in using the Internet for travel information.

Bonn, Furr, and Susskind (1999) found that age, income, educational level,
lodging, and spending are statistically signiﬁcant variables that explain differences
between Internet users and nonusers. The majority (72%) of Internet users who sought
travel information were younger than 45 years of age. Similar ﬁndings are reported by
Weber and Roehl (1999) and Hsu, Kang, and Wolfe (2002). Weber and Roehl (1999)
found that age, income, occupation, and experience with the Internet are statistically

signiﬁcant variables that distinguish on—line from off-line travel information searchers.

16

Those who used the Internet for travel information and purchased travel products/services
were likely to be 26 to 36 years of age, whereas those who neither used the Internet nor
purchased travel products/services were likely either to be younger than 25 or older than
55. Hsu, Kang, and Wolfe (2002) also found that 24% of adults aged 65 or older had
accessed the Internet for travel information, whereas the majority (77%) of adults aged 18
to 24 used the Internet for travel information. This may be because older people are
generally uncomfortable with and reluctant to use new technology (Lewis, 1996; Vuori &
Holmlund-Rytkonen, 2005).

Prior research has also shown that biological age is correlated with travel or
leisure expenditures. Agarwal and Yochum (1999) examined racial differences in the
spending patterns of overnight travelers to Virginia Beach, an Atlantic coast beach resort.
The results of the study indicated that the older travelers, the more they spent. This
ﬁnding is somewhat contradictory to other studies. Thrane (2002) investigated the
relationship between jazz festival visitors and their travel expenditures. The older the
participants, the less they spent. Lehto, Cai, O’Leary, and Huan (2004) found that
Taiwanese tourists aged 20 to 29 spent the most money while those aged below 19 or
above 60 spent the least. Similarly, Weagley and Huh (2004b) found evidence that
biological age has a negative impact on leisure expenditures because the declining health
of older people limited their participation in leisure.

Biological age inﬂuences travel propensity, propensity to search for information
online, and travel expenditures on trips. Although prior studies provide some evidence of
the impact of biological age, few studies have examined travel behavior and biological

age over time.

17

The Time of Travel
The time of travel refers to unique events/incidents or environmental changes that
could affect travel behavior. The time of travel is critical because economic
circumstances, gasoline prices, and technology developments are inﬂuential on travelers
at the macro level. The discussion here focuses on economic circumstances, gasoline

prices, and computer usage during 1997 and 2002 which are the focus of this study.

The Year 1997

Economic Environment in 1997. As summarized in Table 1, the 1997 economic
indicators showed a combination of strong growth in gross domestic product, personal
consumption expenditures, and disposable personal income. The nominal gross domestic
product rose 5.8 percentage points in 1997. Similarly, the real gross domestic product
increased 4.7 percentage points. The unemployment rate declined 9.3 percentage points,

from 5.4 percent to 4.9 percent.

Table 1. US. Economic Indicators in 1997

 

1996—1997
Indicator (billions of dollars) 1996 1997 Change
Nominal Gross Domestic Product $8,113.8 $8,586.7 5.8%
Real Gross Domestic Product $8,536.1 $8,936.2 4.7%
Real Personal Consumption Expenditures $5,619.4 $5,83 l .8 3.8%
Real Disposable Personal Income $5,688.5 $5,988.8 5.3%
Consumer Price Index 158.6 161.3 1.7%
Unemployment Ratea 5.4% 4.9% —9.3%

 

Sources: Bureau of Economic Analysis, Bureau of Labor Statistics.

a. Ages 16 years and older.

18

Gasoline Prices. Gasoline prices have long been believed to play an important
role in peoples’ trip behavior because U.S travelers have traditionally used automobiles
as their primary form of transportation (Corsi & Harvey, 1979; Holecek, 2001; Huh, Lee,
Kim, & Holecek, 2002; Kamp et al., 1979; Morgan, 1986; Williams et al., 1979).
According to the Energy Information Administration (http://www.eia.doe.gov), the
nationwide average gasoline price for a gallon of regular unleaded was $1.20 in 1997, '
with an average of $1.23 per gallon in January and $1.20 per gallon in December. The
gasoline prices declined 2.4% by the end of 1997.

In Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin, average gasoline
price for a gallon of regular unleaded started at $1.27 per gallon in January, 1997, which
was a bit higher than the nationwide average, and fell to $1.19 per gallon in December,
1997, which was a bit lower than the nationwide average. The Midwestern average
gasoline price throughout 1997 was $1.21. The changes in gasoline prices from January
to December during 1997 are summarized in Figure 2. The changes in gasoline prices in
1997 would appear to have been too small to have had a signiﬁcant impact on travel

behavior.

19

 

Gasoline Price
(cents per gallon)
128 -

127
126
125
124
123
122
121
120
119

118 l I l l l 1 L L l l I Month
Jan Feb Mar Apr‘May Jun Jul Aug Sep Oct Nov Dec

+ Nationwide
+ Midwest

 

 

 

 

 

 

Figure 2. The Changes in Gasoline Prices in 1997

Computer and Internet Penetrations. Travel is an information—based activity
(Connolly et al., 1998). People use the Internet to gather information for trip planning and
to purchase travel products and services (Huh, Li, Kim, & Holecek, 2003). For this
reason, the penetration of computer and Internet use are believed to be good indicators of
travel behavior.

According to the National Telecommunications and Information Administration
(McConnaughey, 1997), the computer penetration rate signiﬁcantly increased between
1994 and 1997; there was a 52% increase in personal computer ownership, a 139%

increase in modem ownership, and a 397% increase in E—mail access. Between 1996 and

20

1997 Internet use in the US. increased by 70%, from 30 million in 1996 to 51 millions in

1997, according to the Travel Industry Association of America (Mish, 2003).

The Year 2002

Economic Environment in 2002. The US economic indicators for 2002 are
presented in Table 2. Real Gross Domestic Product increased 2.4 percentage points
between 2001 and 2002. The national unemployment rate rose to 5.8% in 2002 due to the
economy’s overall weakness. Yet, real disposable personal income grew 4.2 percentage
points. Real personal consumption expenditures also rose 3.1 percentage points. CPI

averaged a 1.6 percentage point growth in 2002.

Table 2. The US. Economic Indicators in 2002

 

2001—2002
Indicator (billions of dollars) 2001 2002 Change
Nominal Gross Domestic Product $10,082.20 $10,446.20 3.6%
Real Gross Domestic Product $9,214.50 $9,439.90 2.4%
Real Personal Consumption Expenditures $6,377.20 $6,576.00 3.1%
Real Disposable Personal Income $6,748.00 $7,032.00 4.2%
Consumer Price Index 177.1 179.9 1.6%
Unemployment Rate8 4.7% 5.8% 23.4%

 

Sources: Department of Commerce, Bureau of Economic Analysis; Department of Labor, Bureau of
Labor Statistics.
a. Ages 16 years and older.

Gasoline Prices. As summarized in Figure 3, in 2002 the natibnal price of regular
unleaded gasoline started at an average of $1.11 a gallon in January; rose 34 cents to
$1.45 in October, and fell six cents to $1.39 in December. The overall nation-wide
average gasoline price was $1.34 in 2002. In the Midwestern states, regular unleaded

gasoline price started at $1.10 a gallon in January 2002, and rose to $1.35 per gallon in

21

December, 2002, which was a bit below the nationwide average. The Midwestern

average gasoline price for the year was $1.33 in 2002.

 

Gasoline Price
(cents per gallon)

152 r
148 r
144 r
140 -
136 -
132 -
128 t
124 -

   
 

+ Nationalwide

 

 

120 +Midwest
116

112

108 r

104 r

100 1 ‘ ' ‘ ' ‘ ‘ ' ‘ ‘ ' Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

 

 

 

Figure 3. The Changes in Gasoline Prices in 2002

Computer and Internet Penetration. The number of personal computers in the
US. exceeded 403 million in 2002 according to the Computer Industry Almanac
(Worldwide Cumulative Pc Sales Exceed 1 Billion). Internet use rose by 2.7%, ﬁ'om 110
million in 2001 to 113 million in 2002. Surprisingly, Internet use for travel information
increased by 122% from 51 million users in 1997 to 113 million users in 2002 according
to the Travel Industry Association of America ("Travelers' Use of the Internet, 2003
Edition," 2003). This Travel Industry Association of America report indicated that 29%

of those who traveled in 1997 used the Internet for travel purposes (e.g., reservations,

22

ticketing, information searching, etc.). Two-thirds (67%) of those who traveled in 2002
used the Internet, an increase of 38 percentage points from 1997 to 2002. The Internet has
become an indispensable tool for travelers. For this reason, the penetration of Internet use
is believed to play a central role in travel behavior over time.

The years 1997 and 2002 were economically strong. Gasoline prices were also
stable. However, the penetration of computer and Internet use was remarkable during this

ﬁve-year period.

Generational Cohort

The generational cohort variable used in this study is designed to capture changes
in behavior produced by differences that arise from the life experience, history, values,
attitudes, and socialization of people over time. The importance of generation cohort in
marketing is that it facilitates understanding of different cohort group patterns of
behavior/attitudes and distinguishes one generational cohort from others (Glenn, 1977).
Additionally, marketers have used generational cohort as a marketing tool (Kotler &
Armstrong, 1996). Although the terms “generation,” “cohort,” or “generational cohort”
have been interchangeably used in the literature, distinct studies of the generational
cohort by Strauss and Howe (1991) and Meredith, Schewe, and Karlovich (2002) give
useful insights into these terms and concepts, and practical implications for research.

Strauss and Howe (1991) deﬁned “generation” as a cohort group (everyone born
in a limited span of consecutive years) whose length approximates the span of a phase of
life and whose boundaries are ﬁxed by peer personality (a generational persona

recognized and determined by age location, beliefs and behavior, and perceived

23

membership in a common generation). They divided people into several generational

groupings (approximately 20 to 25 birth-years span) based on the length of a generation,

as well as into four types of generational personalities (i.e., civic, adaptive, idealist, and

reactive). According to their study (1991: 73—74), ﬁve out of the eighteen generations

with a cycle of four personalities had been recognized in the literature:

G. 1. Generation

Those who were born between 1901 and 1924 lived through the Great Depression
and World War II in adulthood. Their peer personality belonged to the civic
generation, which was characterized as growing up as protected youths after a
spiritual awakening; coming of age amid a secular crisis; united into a heroic and
achieving cadre of rising adults; sustaining that image while building institutions
as powerful mid—lifers; and emerging as busy elders attached by the next spiritual
awakening. Some examples of this generation are Billy Graham, Jimmy Carter,

and Lee Iacocca.

Silent Generation

Those who were born between 1925 and 1942 went through the Great Depression
and World War II in their youth and then economic booms in midlife. In addition,
this adaptive generation allocated to their peer personality is described as growing
up as overprotected youths during a secular crisis; maturing into risk-averse,
conformist rising adults; producing indecisive midlife arbitrator—leaders during a

spiritual awakening; and maintaining inﬂuence (but less respect) as sensitive

24

elders. Living members of this generation are Woody Allen, Phil Donahue, Ted
Koppel, James Brown, Aretha Franklin, Gloria Steinem, George Carlin, and Bob

Dylan.

Boom Generation

Those born between 1943 and 1960 have lived through economic booms in
adulthood. They are members of the idealist generation, raised as increasingly
indulged youths after a secular crisis; whose coming of age inspires a spiritual
awakening; fragmenting into narcissistic rising adults; cultivating principle as
moralistic midlifers; and emerged as visionary elders guiding the next secular
crisis. Cerebrated ﬁgures in this generation are Donald Trump, Oprah Winfrey,
Steven Jobs, and Bill Gates, Bruce Springsteen, George W. Bush, Bill Clinton,

and Madonna.

13th Generation

Those born between 1961 to 1981 experienced economic distress in their youth.
Their peer personality is called the reactive generation, which grew up as
underprotected and overcriticized youths during a spiritual awakening; matured
into risk-taking, alienated rising adults; mellowed into pragmatic midlife leaders
during a secular crisis; and maintained respect (but less inﬂuence). Celebrated
ﬁgures are Tom Cruise, Tatum O’Neal, Michael Jordan, Tiger Woods, Venus

Williams, Serena Williams, and Kanye West.

25

o Millennial Generation
Those born between 1982 to 2002 have experienced economic booms and rapid
technology development in their youth. Based on the four cyles of generational
peer personality, the title civic generation should be allocated to this generation,

which has the same chariteristics as the G.I generation.

The generation classiﬁcation of Strauss and Howe (1991) have been adopted to
empirical studies in tourism liteatrue. These studies have dealt with: (1) changes in
activity preferences for pleasure travel over time (Pennington-Gray & Kerstetter, 2001;
Pennington-Gray & Lane, 2001; Pennington-Gray & Spreng, 2001; Warnick, 1993); (2)
cohort segmentation (Pennington-Gray, Fridgen, & Stynes, 2003).

The term “generational cohort” appeared in a book by Meredith, Schewe, and
Karlovich (2002). They deﬁned the generational cohort as “the people we are born with,
travel through our lives with, and experience similar events with, especially those events
that are happening when we are coming of age that imprints core values” (p. 6). The
distinct difference between the concepts of generation (Strauss & Howe, 1991) and
generational cohort (Meredith et al., 2002) is that the generational cohort is classiﬁed into
shortened generations (approximately 7 to 10 birth-years span) and emphasize “deﬁning
moments” that can embrace “wars, political dislocations, assassinations, economic
upheavals, or technological changes (e. g., invention of the atom bomb, automobile, radio,
the rise of television, computer, or the Internet)” (pp. 6—7). The characteristics of their

seven generational cohorts (2001: 14—15) are as follows:

26

Depression Cohort

Members were born between 1912 and 1921. This group’s coming-of—age
experience consisted of economic strife, elevated unemployment rates, and the
need to take menial jobs to survive. Financial security—what they most lacked

when coming of age-rules their thinking.

World War II Cohort

Members were born between 1922 to 1927. Sacriﬁce for the common good was
widely accepted among members of the World War II Cohort, as evidenced by
women working in factories and men going off to ﬁght. This cohort was focused
on defeating a common enemy, and its members are more team-oriented and

patriotic.

Postwar Cohort

People who were born between 1928 to 1945 experienced a time of remarkable
economic growth and social tranquillity, a time of family togetherness, school
dress codes, and moving to the suburbs. While there were some elements of
unrest (the Korean War, McCarthysim), overall this was a pretty tranquil time,

which is why this is such a long cohort.

Leading-Edge Baby Boomer Cohort
Those who were born between 1946 and 1954 remember the assassinations of

John and Robert Kennedy and Martin Luther King, Jr. The death of JFK ﬁrst

27

shaped this cohort’s values. They became adults during the Vietnam War and
watched as the ﬁrst man walked on the moon. Leading-Edge Boomers are very
aware of their cohort grouping, and are very self-assured and self-centered. They
championed causes with fervor because they were sure of being right (Greenpeace,
civil rights, women’s liberation), and felt equally justiﬁed in being hedonistic and
self—indulgent (marijuana, “free love,” sensuality). Because of their position
following the “birth dearth” of the Depression years, they had (and continue to
have) an inﬂuence on society disproportionate to their numbers, being smaller as

a group than the later Boomer cohort that followed.

Trailing—Edge Baby Boomer Cohort

Those who were born between 1955 to 1965 witnessed the fall of Vietnam,
Watergate, and Nixon’s resignation. The oil embargo, a raging inﬂation rate, and
the more than 30% decline in the S&P Index led these individuals to be far less
optimistic about their ﬁnancial future than the Leading-Edge Boomers, whom

they feel got the best opportunities in jobs, houses, and investments.

Generation X Cohort

Those born between 1966 to 1976 are the latchkey children of the ’805, who have
received the most negative publicity. Perhaps because many have seen ﬁrst-hand
the trauma of divorce, this cohort has delayed maniage and children, and they do
not take commitments lightly. More than other groups, this cohort accepts cultural

diversity and puts quality of personal life ahead of work life. They are “free

28

agents,” not “team players.” Despite a rocky start into adulthood, this group

shows a spirit of entrepreneurship unmatched by any other cohort.

o N Generation Cohort
Those born from 1977 to the present are the “N Generation,” or “N-Gens,”
because the advent of the Internet is a deﬁning event for them, and because they
will be the “engine” of growth over the next two decades. They are also known as
“Gen Y” or “Millenials,” and while still a work in progress, their core value
structure seems to be quite different from that of Gen X. They are more idealistic

and team-oriented.

This generational cohort classiﬁcation was applied to a study by Beldona (2005),
who examined changes in travel information search behavior online between 1995 and
2000, using Baby Boomer and Generation X as generational cohorts. The comparison
between generation and generational cohort classiﬁcations are shown in Table 3.

Generational cohort can be a good indicator of changes in individuals’ behavior or
attitudes over time because it give us a macrolevel understanding of peoples’ behaviors,
attitudes, and values, and broaden marketers’ ability to proﬁle their target market. This
study utilizes the concept of generational cohort deﬁned and identiﬁed by Meredith et a1.

(2002) to interpret the results of the study.

29

Table 3. Comparison of Generation and Generational Cohort Classiﬁcations

 

 

 

 

 

 

 

 

 

Strauss & Howe (1991) Meredith et a1. (2002)
Generation Key Events Cohort Coming of Deﬁning Moments
(Birth Years) (Birth Years) Age Years
G.I. Generation World War 1 Depression 1930—1939 Great Depression
(1901—1924) Prohibition Cohort
Depression (1912—1921)
“'0’“ war H World War 11 1940—1945 World War II
Cohort
(1922—1927)
Silent Afﬂuence Postwar 1946—1962 End of World War 11
Generation Civil Rights Cohort Good economic times
(1925—1942) (1928—1945) Moving to the suburbs
Cold War
Korean War
McCarthyism
Emergence of rock and roll
Civil rights movement
Boom Kent State Leading-Edge 1963—1972 Assassinations of JFK, RFK
Generation Watergate Baby Boomer and Martin Luther King, Jr.
(1943—1960) Cohort Vietnam War
(1946—1954) First man on the moon
Trailing—Edge 1973—1983 Fall of Vietnam
Baby Boomer Watergate
Cohort Nixon resigns
(1955—1965) Energy crisis
13th Generation Not yet Generation X 1984-1994 Reaganomics
(1961-1981) known Cohort Stock market crash of 1987
(1966—1976) Challenger explosion
Fall of Berlin Wall
Gulf War
AIDS crisis
Millennial Not yet N Generation 1995—? The Internet
Generation known Cohort Good economic times
(1982—2002) (1977—?) Columbine school shootings

 

 

 

 

Clinton's impeachment
9/11 terrorist attack

 

Source. Meredith, Schewe, and Karlovich (2002, p. 346)

30

Cohort Analysis as a Research Method
To investigate changes in individuals’ behavior or attitudes over time, cohort
analysis has been utilized (Glenn, 1977). Cohort analysis can address the core issue of
changes in individuals’ behavior or attitudes and make it easy to draw inferences about
the changes in behavior or attitudes within a subgroup (Firebaugh, 1997). The discussion
here begins with the concepts of cohort, cohort analysis, and statistical methods that have

been developed in the literature.

Concepts of Cohort Analysis

Social scientists deﬁne a cohort as “the aggregate of individuals (within some
population deﬁnition) who experienced the same event within the same time interval”
(Ryder, 1965, p. 845). Glenn (1977) deﬁnes cohort as “those people who within a
geographically or otherwise delineated population who experienced the same signiﬁcant
life event within a given period of time” (p. 8). The period of time may begin and end at
any point, depending on the data. In general, cohort means birth cohort unless otherwise
indicated.

According to Glenn (1977), the term “cohort analysis” refers to “the
characteristics of one cohort at two points in time” (p. 9). For instance, those who were
born between 1959 to 1963 can be studied in 1997 when they were 33—37 years old, and
then in 2002 when they were 38—42. However, several researchers extend Glenn’s cohort
analysis to biological age, period (time of travel)‘, and generational cohort (birth cohort)

not only to examine changes in individuals’ attitudes or patterns of behavior at two or

 

1 Period has the same meaning as time of travel. Several authors have used “period” in place of a time of
travel.

31

more points in time but also to determine which of the three variables accounts for
observed variation (Chen, R. et al., 2001; Firebaugh, 1997; Firebaugh & Chen, 1995;
Pennington-Gray & Spreng, 2001; Rentz et al., 1983). In addition, cohort analysis is
recommended to be useful in examining changes in behavior or attitudes over time with
respect to biological age, period (time of travel), and generational cohort (birth cohort)

without the need to track the same individuals over time.

Analytical Methods in Cohort Analysis

Several statistical methods developed for cohort analysis have been used to
disentangle biological age, period (time of travel), and generational cohort effects.
However, attempts to separate these three effects at the operational level are difﬁcult

because of an “identiﬁcation problem.”

Year of birth (cohort) = Year of measurement (period/time of travel) — Age

As shown in equation above, the identiﬁcation problem occurs when age and
period (time of travel) are given, generational cohort is spontaneously deﬁned. In other
words, year of birth, or generational cohort, is a linear function of year of measurement
(period/time of travel) and age (Firebaugh, 1997; Hagenaars, 1990). For this reason,
several statistical methods have been developed or applied to solve the identiﬁcation

problem. The following discussion covers each of these statistical methods.

32

Standard Cohort Table Method. This method analyzes changes in individuals’
behavior or attitudes over time by “a table in which the intervals between the points in
time for which there are data correspond in years with the intervals used to delineate the
birth cohorts” (Glenn, 1977, p. 10). In other words, the biological age effect can be traced
by reading cross-sectionally; the period (time of travel) effect can be read vertically;
generational cohort effect can be read diagonally. For instance, the data in Table 4 are
trip propensity of respondents who said how many pleasure trips they took during the
past 12 months to any destinations in response to a question asked on the Michigan
Market Survey in 1997 and 2002. Five-year generational cohorts and ﬁve-year age
groups are used to correspond with the ﬁve-year intervals between the two survey years.
As shown in Table 4, the standard cohort table indicates that “travel propensity (number
of pleasure trip taken in the past 12 months)” did not change during the ﬁve-year period
(mean = 5.6 in 1997 vs. mean = 5.5 in 2002). Among age groups in 1997, the differences
among the 48—52 years-old group (mean = 6.0), the 53—57 years-old group (mean = 6.3),
and the 63—67 years-old group (mean = 6.0) were the largest, whereas in 2002, the
differences among the 23-27 years-old group (mean = 6.3) and the 58—62 years-old
group (mean = 6.3) were the largest. Among the inter-generational cohort comparisons,
the 1930—1934 generational cohort appeared to be the most changed cohort (mean = 6.0
in 1997 vs. mean = 4.9 in 2002), followed by the 1960—1964 generational cohort (mean =
5.9 in 1997 vs. mean = 4.9 in 2002) and the 1975—1979 generational cohort (mean = 5 .6
in 1997 vs. mean = 6.3 in 2002).

The standard cohort table method can be used to analyze changes without any

statistical analyses, but depending on the available data, it may not be applicable due to

33

inconsistent questionnaire design, survey years, or both. The standard cohort table
method has been used in several studies, which included the changes in travel
information search online from 1995 to 2000 (Beldona, 2005), cohort segmentation of
travel preferences (Pennington-Gray et al., 2003), travel life cycle (Oppermann, 1995),
the changes in soft drink consumption (Rentz et al., 1983), and the applications of cohort

analysis to marketing problems (Reynolds & Rentz, 1981).

Table 4. Sample of Standard Cohort Table for Travel Propensity

 

 

 

1997 (n=2,516) 2002 (n=3,219)

Age Cohort Mean Age Cohort Mean
18 — 22 1975 — 1979 5.6’1 18 — 22 1980 — 1984 5.8
23 —- 27 1970 — 1974 5.4 23 - 27 1975 - 1979 6.3
28—32 1965- 1969 5.1 28-32 1970-1974 5.2
33—37 1960—1964 5.9 33—37 1965—1969 5.4
38—42 1955—1959 5.1 38—42 1960-1964 4.9
43 — 47 1950 - 1954 5.6 43 — 47 1955 —1959 5.6
48 — 52 1945 — 1949 6.0 48 — 52 1950 — 1954 5.8
53 — 57 1940 — 1944 6.3 53 — 57 1945 — 1949 5.6
58-62 1935- 1939 5.6 58-62 1940—1944 6.3
63 — 67 1930 — 1934 6.0 63 — 67 1935 —1939 5.2
68 or older 51929 4.6 68 or older 51934 4.9

Total 5.6 5.5

 

 

Note. Respondents who were 18 — 22 years old group in the 1997 survey belonged to the 23 — 27
years old group in the 2002 survey, so they represented the generational cohort years of 1975 to
1979.

a. Each cell is mean of trip propensity (number of pleasure trip taken during the past 12 months) by
years.

Triad Method. Palmore (1978) developed a “triad method” that illustrates three
levels of analysis to avoid conceptual and operational confusion of cohort analysis. It
results in observable differences, infers which effects produced the differences, and

imputes causes of the effects (Reynolds & Rentz, 1981). The ﬁrst level of analysis is to

34

compute the observable differences and then to employ the independent samples t-test to
ﬁnd statistical differences between them. The observable differences are deﬁned as
follows:

0 Longitudinal differences are those between early and later measurements on the
same generational cohort. For example, the differences are measured between
those born between 1975 to 1979 in 1997 and 2002 over a ﬁve-year study period.

c Cross-sectional differences are those between younger and older age groups at
one point in time. For example, the differences are measured in 2002 between
those aged 18 to 22 and 23 to 27.

o Time-lag differences are those associated with the same age groups at two
measurement times. For instance, the differences are measured for those aged 18

to 22 in 1997 and again in 2002.

The second level of analysis involves segmenting the observed differences into
biological age, period (time of travel), and generational cohort effects. Longitudinal and
cross-sectional differences indicate the biological age effect; longitudinal and time-lag
differences indicate the period (time of travel) effect; cross-sectional and time-lag
differences indicate the generational cohort effect. The ﬁnal analysis is to impute causes
for these effects.

The triad method has been applied in several studies. Reynold and Rents (1981)
applied it to study the application of cohort analysis to marketing problems. Pennington-

Gray, Kerstetter, and Wamick (2002) used the triad method to forecast the international

35

vacation patterns of Canadian travelers. Beldona (2005) employed the triad method to

examine changes in travel information search behavior over time.

Analysis of Variance Method. Analysis of variance identiﬁes the variation among
independent variables. For example, biological age can be tested by conducting One-way
analysis of variance (One-way ANOVA), and, if any signiﬁcant mean differences are
identiﬁed among age groups, then a post-hoe test can be used to identify which age group
is different from that of other groups. However, applying One-way ANOVA to each
effect respectively can not determine which effect is dominant over time.

There are some empirical studies that have employed this method, including
cohort segmentation in travel preferences‘of Canadians (Pennington-Gray et al., 2003),
Japanese travelers’ behavior changes over time (You & O'Leary, 2000), and the

application of cohort analysis to marketing problems (Reynolds & Rentz, 1981).

Constrained Multiple Classification Method. The constrained multiple
classiﬁcation method, proposed by Mason et al.(1973 ) and further studied by Chen et a1.
(2001), Rentz and Reynolds (1991), and Rentz et a1. (1983), was developed to separate
and estimate the effects of biological age, period (time of travel), and generational cohort

simultaneously by using multiple regression. The model of this method is as follows:

Yijk = u+ai+pj+bk+eijk (2.1)

36

Yijk is the dependent variable; the effect of ith biological age is given by 61,-, the
effect of jth period (time of travel) by pj, and the effect of kth generational cohort by bk;

u is the grand mean of the dependent variable, and eijk is the random error. i, j, and k

were coded into i—1,j-1, and k-l dummy variables, respectively.

As shown in model (2.1), when biological age and period (time of travel) are
given, generational cohort is spontaneously deﬁned (e.g., generational cohort equals
period minus biological age). This is the “identiﬁcation problem” (Firebaugh, 1997;
Glenn, 1977). Mason et a1. (1973) suggested constraining the model with at least two age
groups, two periods, or two generational cohorts, to estimate the parameters. The choice
of selectively constraining categories in the model is arbitrarily based (Mason et al.,
1973), on outside information (Firebaugh, 1997; Glenn, 1977), or on “theoretical and
substantive grounds” (Rentz & Reynolds, 1991, p. 360).

This method requires engaging more statistical techniques, but it is less
complicated and more intuitive for interpreting the effects of biological age, period (time
of travel), and generational cohort over time than other cohort analytical methods. The
constrained multiple classiﬁcation method has appeared in such tourism and marketing
studies as population aging and Japanese international travel (Mak et al., 2005; Sakai et
al., 2000), changes in travel preferences of Canadians (Pennington-Gray & Spreng, 2001),

changes in soft drink consumption over time (Rentz et al., 1983).

37

Cohort Studies in Tourism Research

Since Ryder’s article, “The Cohort as a Concept in the Study of Social Change,”
appeared in a 1965 many cohort studies have been conducted in the ﬁelds of social and
behavioral sciences, such as changes in cigarette smoking (Chen, X. et al., 2003; Kemm,
2001), changes in value preferences (Hellevik, 2002), life insurance purchasing patterns
(Chen, R. et al., 2001), saving behavior of US. households (Attanasio, 1998), women’s
voting rates (F irebaugh & Chen, 1995), changes in attitude toward working women
(Misra & Panigrahi, 1995), changes of sociopolitical orientations (Alwin & Krosnick,
1991), and shifts in party identiﬁcation among Southern Whites (Cassel, 1977).

However, until Reynolds and Rents (1981) introduced their cohort analytical
method, marketing researchers did not recognize cohort analysis as a marketing research
tool. Their article applied cohort analysis to strategic planning in marketing. They
successfully used the triad method (Palmore, 1978) to separate the effects of age
(biological, psychological, social), period (environmental change, e.g., marketing,
changes in measurement), and cohort (historical differences in socialization, genetic
change, cohort composition) on the changing role of women in the home by using
marketing data from 1969, 1975, and 1979. They suggested that cohort analysis could
classify relationships between age effect, period effect, and cohort effect, and predict the
direction of marketing environmental changes as the US population ages.

Rentz, Reynolds, and Stout (1983 59) employed the cohort analytical method to
identify and anticipate changes in the consumption patterns of Coca—Cola by using a data
set that included: 1950, 1960, 1969, and 1979. They implemented a constrained

regression method to estimate the effects of age, period, and cohort over time. Their

38

results indicated that the future consumption of soft drinks would not decline because of
the large younger cohorts’ replacements, which was in contrast with the assumption that
soft drink consumption would decline because of an aging population. They suggested
that marketing researchers should use a cohort analytical method, especially when they
forecast customer consumption patterns based on population changes. A similar study
was conducted by Bonnici and Fredenberger (1991; 1992), who introduced a cohort
analysis to examine and forecast changes in food consumption patterns by using a data
set for the period 1979 to 1989. The ﬁndings of the study indicated that the age effect had
the most signiﬁcant impact on food consumption patterns over time. The authors
suggested that cohort analysis works well with secondary data; Palmores’ method is
relatively simple; data collection requires less time and money. Hansman and Schutjens
(1993) investigated wine consumption patterns in Netherlands by using the cohort
analytical method. They found that period has the most dominant effect on wine
consumption.

Cohort studies are scarce in the tourism and hospitality literature. Early examples
of cohort studies in the tourism and hospitality literature appear with Oppermann (1995),
who examined the changes in German travelers’ patterns and destination choice with
respect to the effects of biological age, time of travel, and generational cohort. Biological
age was used to account for variations in travel behavior over the life span of an
individual; the time of travel was used to reﬂect annual changes and/or speciﬁc events
that inﬂuenced tourism behavior; generational cohort was used to account for travel
behavior patterns due to some unique aspect of that cohort’s historic background. The

ﬁndings indicated that destination choice, the purpose of trip, and travel month differed

39

with respect to biological age. The younger generational cohorts were found to take more
trips and to travel farther than did previous generational cohorts. He emphasized that a
longitudinal study such as his gives worthwhile insights into changing destination choices
over time to destination marketers and practitioners.

You and O’Leary (2000) added to Oppermann’s ﬁndings by observing Japanese
senior outbound travelers’ propensity to travel, destination activity participation, and
travel philosophy with respect to biological age (i.e., age 55—64 year-old group in both
1986 and 1995) and generational cohort (i.e., those born between 1931 to 1940) using a
Canadian data set for the period 1986 to 1995. One-way ANOVA and Multivariate
Analysis of Variance were used in the study. The generational cohort effect dominated
the biological age effect. There was no evidence that travel propensity would decline with
age. This ﬁnding is consistent with Oppermann’s assumption that each generational
cohort holds different travel propensities through the life span brought on by different
travel experiences and values. They concluded that today’s older generational cohort
would participate in more activities at their destinations than did earlier older
generational cohort. They would be more likely to be interested in “culture and heritage”
and “shopping” at the destination.

Another Japanese travel study of seniors, conducted by Sakai et a1. (2000),
examined the effects of biological age and generational cohort on international travel
propensity. Biological age, generational cohort, and other determinate variables (i.e.,
wage, exchange rate, and labor force) were used to estimate variations in Japanese
overseas travel propensities by men and women. Multiple regression with ordinary least

square (OLS) was employed in their analysis. Biological age and generational cohort

4o

were found to be signiﬁcant determinants of international travel for Japanese men and
women. They found that generational cohort was more dominant than biological age,
which is consistent with the ﬁndings of Oppermann (1995) and You and O’Leary (2000).
Thus, Sakai et a1. (2000) concluded that Japanese international travel propensity should
not decline as the Japanese population ages.

Pennington-Gray is one of the pioneers who have applied cohort research to
tourism and hospitality. Pennington-Gray and Kerstetter (2001) investigated whether the
effect of two senior Canadian generational cohorts (i.e., 55 to 64 years old named
“Depression Babies,” 65 years old or older named “Roaring Twenties Generation”) and
time of travel (i.e., 1983 and 1995) caused changes in travel preferences during a 13-year
period. Analysis of variance was employed. Generational cohort and time of travel effects
played a signiﬁcant role in the changes of senior Canadians’ travel preferences. For

instance, the generational cohort, “Depression Babies,” preferred “beaches for swimming

9, 6‘ 99 66

and sunning,” “budget accommodations, shopping, nightlife and entertainment,” and
“theme parks and amusement parks” more than their counterpart, “Roaring Twenties
Generation”. Signiﬁcant changes in travel preferences between 1985 and 1995 were also
found. These ﬁve preferences were more popular in 1995 than in 1985.

Pennington-Gray and Spreng (2001) extended the previous study using the same
longitudinal Canadian dataset but employing a constrained multiple classiﬁcation
technique of cohort analysis. In that study, biological age, time of travel, and generational
cohort were used to estimate variations in travel preferences over time. The model that

included biological age and generational cohort explained “national/provincial parks” and

“high quality restaurants” preferences better than any other model. In other words,

41

biological age and generational cohort have a signiﬁcant impact on the preferences for
the national/provincial parks and ﬁne restaurants. Time of travel and generational cohort
were also found to have a signiﬁcant impact on the preference for First-class
accommodations. The time of travel model explained “shopping” better than any other
model. The model that included biological age, time of travel, and generational cohort ﬁt
well with the preference for “museums and art galleries.” These ﬁndings are consistent
with another study of Pennington-Gray, Fridgen, and Stynes (2003), who used the cohort
method to segment the Canadian travel market by using the same Canadian dataset. The
analytical methods used in that study were t-test, ANOVA, and standard cohort table.

Another cohort study by Pennington-Gray, Kerstetter, and Warnick (2002)
employed the triad technique of the cohort analysis to investigate changes in international
travel patterns of the US. residents over time. Secondary data collected from Simmons
Market Research Bureau were used for the analysis. Time of travel and generational
cohort were found to have a dominant impact on the participation of international
vacation travel. They implied that the interaction of time of travel and generational cohort
would inﬂuence the frequency of international travel.

Beldona (2005) examined travelers’ online information search behavior by using
a cohort analysis. Secondary data from 1995 and 2000 were used for the study. The
analytical methods used in the study were ANOVA and triad. No statistically signiﬁcant
biological age and generational cohort effects were found across the ﬁve-year study
interval. However, a signiﬁcant time of travel effect was found. The time of travel had

the strongest impact on propensity to search for travel information online over time.

42

As the review of literature has shown, cohort analysis can provide meaningful
insights on changes caused by biological age, period (time of travel), and generational

cohort over time.

Modeling Studies of Travel Behavior in Tourism Research

The research question raised in this study is that, if the effects of biological age,
time of travel, and generational cohort exist for travel behavior, can these effects be
combined with other factors to explain variations in travel behavior over time. To address
this question, several models were constructed that are assessed in depth in the following
chapter. Rivett (1972) pointed the reason for building a model is “a model is ﬁrst of all a
convenient way of representing the total experience which we possess, of then deducting
from that experience whether we are in the presence of pattern and law and, if so, of
showing how such patterns and laws can be used to predict the future” (p. 1). The
following discussion addresses modeling in tourism literature and the key factors that

have been found to have a signiﬁcant impact on travel behavior.

Travel Propensity Model

Several researchers have studied the relationship between selected factors and
travel propensity and have identiﬁed the underlying factors that have a signiﬁcant impact
on travel propensity (Johnson & Suits, 1983; Mak et al., 2005; Morgan, 1986; Sakai et al.,
2000). Johnson and Suits (1983) modeled the relationship between higher gasoline prices
and the number of visits to US National Parks. They reported that transportation cost (i.e.,

the price of gasoline or private transportation cost) reduced the number of visitors to

43

national parks. Moreover, transportation cost and seasonality were found to be signiﬁcant
determinants in the model. Similar ﬁndings were reported by Morgan (1986), who
replicated a study by Johnson and Suits (1983) to investigate the relationship between
travel costs and the number of visitors to US. national parks with a special focus on
Grand Canyon National Park. A signiﬁcantly negative relationship between travel costs
and the number of visitors was found. Speciﬁcally, private transportation cost, crisis
effect (i.e., oil crisis months), and income were identiﬁed in their model of visitation to
US National Parks. However, the crisis effect was found to be a signiﬁcant factor only in
the model for Grand Canyon National Park. They extended the inquiry to examine the
relationship between travel costs and the number of visitors to the Grand Canyon Park by
transportation mode (private vs. public transportation). The transportation mode was also
found to be a signiﬁcant factor. As gasoline costs rose, park visitors tended to use public
transportation more.

Sakai et a1. (2000) investigated the relationship between biological age, gender,
and international travel propensity among the Japanese. Japanese travel propensity was
deﬁned as “the number of yearly overseas trips divided by the total population” (p. 212).
The male and female models indicated that biological age, generational cohort, exchange
rate, and labor force participation rate were signiﬁcant determinants of travel propensity.
Mak et a1. (2005) replicated the study of Sakai et a1. (2000) to model Japanese
international travel propensity with respect to gender. Their results were similar to the
results of Sakai et a1. (2000). The male model showed that biological age, generational

cohort, exchange rate, and labor force participation rate determined travel propensity.

44

Biological age, generational cohort, and labor force participation rate were found to be
signiﬁcant determinants of travel propensity in the female model.
In sum, biological age, generational cohort, income, transportation mode, travel

costs, exchange rates, and labor force participation rate have been found to affect travel

propensity.

Trip Planning Horizon and Information Search Online Models

To launch timely tourism advertisement programs and promotions, knowledge of
the trip planning interval associated with travel behavior is essential both to tourism
marketers and practitioners. Early examples of related research are studies by Schul and
Crompton (1983) and Gitelson and Crompton (1983).

Schul and Crompton (1983) studied the search behavior of UK international
vacationers with respect to travel-speciﬁc psychographic variables and sociodemographic
variables. They found that cultural interest, comfort, activity, and opinion leadership
explained observed differences in total trip planning intervals. Gitelson and Crompton
(1983) investigated the relationship between trip planning intervals and information
sources used by pleasure vacationers. The length of stay, trip distance from home to the
primary destination, and the purpose of trip (i.e., visiting friends or relatives) were found
to be key factors in trip planning intervals. The longer and the farther from home, the
longer in advance pleasure vacationers planned their trip. Those who traveled to visit
friends and relatives were found to have shorter trip planning intervals. Rao, Thomas, and
Javalgi (1992) found, in a relationship study of activity preferences at destinations and

trip planning behavior for the US. outbound pleasure travel market, that trip expenditures

45

and distance of trip were signiﬁcant factors in explaining trip planning intervals. Higher
trip expenditures and farther distances were found to be associated with longer trip
planning intervals. These ﬁndings are consistent with those of Fodness and Murray
(1997) who found that biological age, the purpose of trip (e.g., vacation, visiting friends
and relatives), transportation mode, length of stay, number of destinations visited,
number of attractions visited, lodging type, and trip expenditures inﬂuence trip planning
horizons. More speciﬁcally, the longer the stay, the more destinations, the more
attractions, the higher the expenditures, were found to be tied to longer trip planning
horizons. However, those who were young, single, visited friends and relatives, and
traveled in their own vehicles had shorter trip planning horizons.

Yoon (2000a) modeled and estimated trip planning intervals of pleasure travelers
in Michigan. Season, party size, activities, length of trip, the purpose of trip, and
expenditures were found to be statistically signiﬁcant in explaining differences in trip
planning intervals. More speciﬁcally, the longer the trip and the higher the travel
expenditures, the longer were respondents’ trip planning horizons. However, those who
visited friends and relatives had shorter trip planning intervals. In sum, key determinants
of the length of trip planning horizons were found by Yoon to be: the duration of trip,
travel expenditures, travel distance, the purpose of trip, party size, and activity
participation.

Since the evolution of the Internet in 19905, several studies have employed
models to identify the key determinants of online travel information search behavior.
Bonn et a1. (1999) observed that key factors between those who used the Internet to seek

travel information online and those who did not were: age, education, museum attendance,

46

sightseeing, computer use, Internet booking, and seeking information about Tampa,
Florida. The ﬁndings of Weber and Roehl (1999) added income, occupation, and number
of years of experience with the Internet. The higher the income, the higher the occupation
status, and the more years of experience with the Internet, the more likely users are to

search for travel information online or to purchase travel products or services online.

Travel Expenditure Model

Agarwal and Yochum (1999) examined predictors of travel expenditures by
testing data collected in Virginia Beach during the summer of 1997. Three multiple
regressions models were tested. The ﬁrst model identiﬁed: (1) income, (2) length of stay,
(3) travel party size, (4) number of children in travel party size, and (5) travelers lodging
in hotels or motels, as variables that explain variations in total travel party expenditures.
The second model identiﬁed: (1) income, (2) travel party size, (3) number of children in
travel party size, and (4) travelers lodging in hotels or motels, as variables that explain
variations in total travel party expenditures per day. In the third model, (1) income, (2)
age, (3) number of children in travel party size, and (4) travelers lodging in hotels or
motels, were found to explain variations in travel expenditures per person per day. They
concluded that regardless of which dependent variables were used in the models, income,
number of children on the trip, and travelers lodging in hotels or motels were the most
important predictors of travel expenditures. Thrane (2002) studied the relationship
between jazz festival visitors and their expenditures and found that music interest, length
of stay, types of festival participants (e.g., tourists, locals), household income, household

size, gender, trip planning horizon, age, and employment status inﬂuence travel

47

expenditures. In another study, age, work status, income, and household education were
found to be related to leisure expenditures (Weagley & Huh, 2004a).

Tyrrell and Johnston (2003) developed a model to analyze trip expenditure
changes after welcome center visits. Income, number of children on the trip, and picking
up travel-related materials were identiﬁed as signiﬁcant variables in their model.
Downward and Lumsdon (2004) examined the relationship between transportation mode
and visitor spending in the North York Moors National Park in the UK. Visitors using
private transportation, duration of trip, and visitors spending over 100 pounds were found
to be signiﬁcant determinants in their model. It is noticeable that few, if any,

sociodemographic variables were assessed in the latter two models.

Conclusions and Implications of Literature Review

The purpose of this study was to investigate changes in patterns of travel behavior
over time by applying cohort analysis. In this study, cohort analysis refers to examining
the effects of biological age, time of travel, and generational cohort. The reason for
adopting cohort analysis for this study was that indicators such as biological age, time of
travel, and generational cohort, which have been found to be signiﬁcant indicators in
many studies, can provide enhanced knowledge about people’s future behavior or
attitudes.

Biological age, according to the ﬁndings of previous studies reported in the
literature, can affect travel behavior, especially behaviors such as travel propensity, trip
planning horizon, propensity to search for travel information online, and travel

expenditures on trips. This is because biological age may cause people to select those

48

activities due to their interest and physical abilities’ changes as they become older. Thus,
in this study it was predicted that biological age will be associated with travel behavior
changes over time, including: travel pr0pensity, trip planning horizon, propensity to
search for travel information online, and travel expenditures on trips.

The time of travel, referring to environmental factors at the time people plan a trip
or take a trip, can inﬂuence travel behavior because economic circumstance, gasoline
prices, and computer penetration and usage can affect travel behavior. In this study, it
was predicted that the time of travel will be associated with travel propensity, trip
planning horizon, propensity to search for information online, and travel expenditures on
trips.

Generational cohort, pertaining to those born with, traveling through their lives
with, experiencing similar or lifetime events at the critical late adolescent and early
adulthood, has been found to be a signiﬁcant factor that can inﬂuence travel behavior
over time, because individual core values, attitudes, and behavior can be difficult to
change. Thus, in this study it was predicted that the generational cohort variable will be
associated with observed differences in: travel propensity, trip planning horizon,
propensity to search for travel information online, and travel expenditures on trips.

Furthermore, a number of studies have found that detenninates of travel behavior
other than biological age, time of travel, and generational cohort can broaden tourism
marketers’ ability to proﬁle their target markets and more accurately match their market
offerings to individuals and groups. This study drew upon the ﬁndings of prior studies to
identify other variables to include in analyses to better explain variations in travel

behavior over time.

49

CHAPTER III
METHOD

The focus of this study was on changes in patterns of travel behavior over time
with a primary focus on the effects of biological age, time of travel, and generational
cohort. The biological age variable captures changes in travel behavior due to physical or
life cycle changes which occur along with aging. The time of travel variable captures
changes in travel behavior due to unique events/incidents or environmental changes that
occur at the time a trip is taken or is being planned. The generational cohort variable
captures changes in travel behavior caused by differences that arise from the unique life
experience, values, and socialization of cohorts.

This chapter discusses the methods employed to obtain the data required to meet
the study objectives and describes the procedural steps taken in preparing and analyzing
these data. The initial sections of the chapter explain why data collected from the
Michigan Travel Market Survey (hereafter, MTMS) were selected for the study, how the
MTMS was conducted, how selected data were prepared for analysis, and what kinds of

analytical methods and procedures were performed during the study.

Data Source
To accomplish the purpose of the study, data were needed for at least two time
intervals. Data collected in the MTMS were utilized for this study because the survey met

this criterion and the data were readily available.

50

The main purposes of MTMS were to evaluate Travel Michigan’s promotional
programs2 and to measure the characteristics and behavior of travelers in Michigan’s
primary market area: the states of Illinois, Indiana, Michigan, Ohio, Wisconsin, and the
Canadian province of Ontario. From January 1996 onward, the MTMS yielded
longitudinal information about travel behavior across the study region. During an eight-
year survey period (1996—2003), a total of 38,417 interviews were completed. The study
was terminated in June 2003 due to budget reductions.

The following sections discuss the detailed information that can be found in
MTMS and how it was collected. The discussion focuses on: the study population and
sampling strategy employed, the study instrument used, data collection and processing

procedures, and response rates.

Study Population and Sampling

The MTMS study population consisted of households in the states of Illinois,
Indiana, Michigan, Ohio, Wisconsin, and the Canadian province of Ontario. Survey
respondents had to be 18 years old or older when interviewed. Random digit-dial samples
of household telephone numbers in the study region were purchased from Survey
Sampling, Inc. for the study. On average, 400 telephone interviews were completed each
month during the study with an associated i 3 percent sampling error (Lohr, 1999).

According to the 2000 US. Census and the 2001 Canada Census, 21 million

households lived in the study region during the study period. Slightly more than one-ﬁfth

 

2 Travel Michigan is the state government organization charged with promoting tourism in the state.

51

(21%) of households resided in Illinois, closely followed by Ohio (20%), Ontario (20%),

Michigan (18%), Indiana (11%), and Wisconsin (10%).

Study Instrument

The questionnaire used in the study was developed for and administered via a
Computer-Assisted Telephone Interviewing (CATI) laboratory maintained by the
Michigan Travel, Tourism, and Recreation Resource Center at Michigan State
University. A core set of questions in the questionnaire remained unchanged during the
eight-year period of the study; other questions relating to the research project were
rotated in and out periodically. The core questions used for this study are presented in
Appendix A. The core questionnaire consisted of the following series of questions:

0 Sociodemographic. This series of questions covered variables such as state of
residence, household composition (i.e., preschool child, school-age child under
age 18, senior citizen, and handicapped person), number of people living together
in the household, number of adults over age 18 in the household, number of full-
time wage-earners living in the household, employment status, ethnicity, and
household income.

0 Trip behavior characteristics. This series of questions covered travel propensity,
month and day trip began, the primary purpose of the trip, type of transportation
used, the number of people in the travel party, respondent’s gender and age, travel
party’s genders and ages, type of trip, number of nights away from home,
number of nights spent in the state or province containing the trip's main

destination, the main type of lodging used, activity participation (e.g., nightlife,

52

visit to a state or national park, visit to a museum or hall of fame, etc.), total travel
expenditures, and the main destination of the trip.

0 Trip planning behavior. This series of questions included variables such as
whether respondents had access to the Internet and obtained travel information

from the Internet. Respondents’ travel planning intervals were also obtained.

Data Collection and Procedures

The data were collected via a Computer Assisted Telephone Interviewing (CATI)
laboratory. The survey employed random digit-dial samples of household telephone
numbers in the study region purchased from Survey Sampling, Inc. The CATI laboratory
was in operation from 6 pm. to 10 pm. Eastern Time (EST) Monday through Thursday
evenings, from noon to 4 pm. EST on Saturday, and from 2 pm. to 6 pm. EST on
Sundays. In the evenings, households in the Eastern time zone were called from 6 pm.
to 9 pm. EST, and households in the Central time zone were called from 7 pm. to 10
pm. EST so that attempts to contact potential respondents in the different time zones
were made within the same time span. In addition, numbers within each time zone were
randomized so interviewers did not call the same state/province throughout their shifts.
Each interviewer was trained to explain the purpose of the study and to ask for informed
consent.

Up to three attempts were made to contact each household in the designated
sample. Interviewers randomly selected respondents within households by asking to
speak to the adult in the home over 18 years old with the next birthday. If that person

was not available, interviewers asked to speak to the person having the following

53

birthday. This procedure was used to minimize the potential bias caused by the tendency
of certain persons in a household to answer the phone more frequently.

The goal of this sampling design was to obtain an unbiased random sample of the
general population in the study region. Subsequent assessments of the quality of the data
revealed no obvious sources of signiﬁcant bias. Still, the potential for bias cannot be

discounted, especially given increases in use of cell phones, answering machines and

Caller ID.

Response Rate

The response rate, including partially completed interviews, was 44%. A test for
possible nonresponse bias in the data revealed few important differences between the
characteristics of a sample of 173 nonrespondents (other than refusals) and a subsample
of 173 randomly selected respondents on 84 variables, including demographic and
socioeconomic characteristics. The only differences between the two groups that were
found to be statistically signiﬁcant at the .05 level of probability were: (1)
nonrespondents were most likely to have visited a state or national park on their most
recent pleasure trip in Michigan (43% vs. 28%), (2) nonrespondents, on the average,
rated the desirability of Ontario as a pleasure trip destination on a 10—point scale more
highly than did respondents (6.7 vs. 5.2), and (3) nonrespondents, on the average, tended

to live in households containing fewer persons than did respondents (2.6 vs. 3.1).

54

Data Preparation

Data Selection Procedures

The following criteria for data selection were applied because at least two-annual
surveys containing the same questions were necessary to address the purpose of the study.
In the initial survey year (1996), questions about “trip planning horizon (number of days
planning began in advance of the trip)” and “propensity to search for travel information
online” were not included, so the 1996 data were therefore excluded. In the fourth year of
the survey (1999), the ﬂow of the questionnaire and the respondents to the survey were
signiﬁcantly different from those of previous years; respondents who traveled in
Michigan during the past 12 months were only interviewed in 1999, so the 1999 survey
was excluded. After this screening process, 1997, 1998, 2000, 2001, 2002 surveys
remained. To identify changes in patterns of travel behavior, the data to be compared
should have the widest interval between survey years. Thus, the data from 1997 and 2002
met the decision criteria of the widest available interval between survey years and
identical question sets.

Other criteria applied to the 1997 and 2002 data were: (1) that the sample should
contain only those respondents who took a pleasure trip during the past 12 months to any
destination, and (2) the trip had to be taken in 1997 and 2002. In the telephone survey,
respondents were asked whether they had taken a pleasure trip in the past 12 months, and
then only those who had taken a pleasure trip during the past 12 months to any
destination were asked which month and day their trip began. These screening questions

were used to select the subsamples for the study.

55

In 1997, 5,668 interviews were completed. Residents of Minnesota were
excluded because Minnesota residents were not interviewed in 2002. After excluding
Minnesota respondents, 4,962 respondents remained, of which, 2,715 had taken a
pleasure trip during the past 12 months. After performing the data cleaning procedures,
2,516 respondents remained in the 1997 data set. Identical data cleaning procedures were
employed to select the 2002 data set of respondents; 5,041 interviews were completed in
2002. Respondents who had taken a pleasure trip during the past 12 months to any
destination accounted for 3,317 out of the 5,041 interviews. Those who took a pleasure
trip in 2002 and met all of the selection criteria amounted to 3,219 respondents.
Ultimately, 5,735 (2,516 from 1997 plus 3,219 from 2002) cases were used for the

analysis.

Data Weighting

The sample drawn for the MTMS was not proportional to the population in the
state or provinces in the region, hence weighting was employed to correct for over and
under sampling of households across the study region. According to the 2000 US. census
and the 2001 Canada census, 21,463,473 households comprised the study region; slightly
more than one-ﬁfth (21.4%) of households resided in Illinois, followed by Ohio (20.7%),
Michigan (17.6%), Indiana (10.9%), Wisconsin (9.7%), and the Canadian province of
Ontario (19.7%). However, in the samples obtained in the MTMS in which Michigan
residents were purposely oversampled, over one-fourth (25.5%) of respondents resided in
the state of Michigan, followed by Ohio (16.4%), Wisconsin (14.8%), Illinois (13.9%),

Indiana (12.6%), and the Canadian province of Ontario (16.8%). Thus, residents who

56

lived in Michigan, Wisconsin, and Indiana were oversampled, but those who resided in
Illinois, Ohio, and Ontario were undersampled. To compensate for this discrepancy, the
data set was weighted using the following procedures. The number of respondents from
Illinois was multiplied by 1.5, which was computed by 1,227 (estimated Illinois samples
= 5,735 total samples X .214 actual proportion of Illinois households from a report of
2000 US census in the study region) divided by 800 respondents of Illinois collected
from the MTMS survey. Using the same weighing procedure, the number of respondents
from the state of Indiana was multiplied by .9; the number of respondents from Michigan
was multiplied by .7; the number of respondents from Ohio was multiplied by 1.3; the
number of respondents of Wisconsin by .7; the number of respondents from Ontario by
1.2. These weightings were applied in the analysis to mitigate the possibility of over or

under sampling bias in the results.

Modification of Variables

Prior to conducting the analysis, the study variables needed to be modiﬁed to
better suit the purposes of the study. Biological age was originally collected using a ratio
scale, but was later converted into the following categories: 18—22, 23—27, 28—32, 33-37,
38-42, 43—47, 48—52, 53—57, 58—62, 63—67, and “68 or older.” Biological age was
categorized into ﬁve-year increments due to the ﬁve year interval in the date set analyzed
(1997 and 2002). For statistical analysis purposes (i.e., multiple regression and logistic
regression), the age group of 68 or older was chosen as a reference group3 because those

respondents are presumably retired and represented a minority in the sample. The time of

 

3 Reference group means the group can be served conceptually as the comparison points and be coded
operationally with “0.”

57

travel variable was the year in which the respondent took his or her pleasure trip. The
year 2002 was selected as a reference year in part because it was inﬂuenced by the
aftermath of the September 11, 2001 terrorist attacks. Generational cohort variables were
categorized into individuals born prior to 1929 or within one of the following ﬁve—year
intervals: 1930—1934, 1935—1939, 1940—1944, 1945—1949, 1950—1954, 1955-1959,
1960—1964, 1965—1969, 1970—1974, 1975-1979, and 1980—1984. Respondents who were
somewhere in the 18—22 age group in 1997 belonged somewhere in the 23—27 age group
in 2002, so they represented the generational cohort born in the years 1975—1979.
Similarly, respondents who were 43—47 years old in 1997 belonged to the 48—52 year old
group in 2002, so they represented the generational cohort born in the years 1950 to
1954. For statistical analysis purposes, the generational cohort of 1980-1984 was
selected as a reference group because these respondents are represented a minority in the
sample.

Dummy variables were used in the analysis. The respondents’ “state of
residence” variable was originally collected in seven categories but were modiﬁed into
“1” if the respondent resided in Michigan and “0” for all other respondents. The variables
“pre-school child in the household,” “school-age child under age 18 in the household,”
“senior citizens” or “disabled people” in the household were also categorical variables.
The “employment status” variable was measured using seven categories but were

‘61,,

modiﬁed into a dummy variable with the following values: if a respondent worked
full-time, part-time, or had some other employment situation; “0” for all other

respondents. Ethnicity was measured by an open-ended question with responses code into

58

six categories. They were modiﬁed into: “1” if the respondent was Caucasian and “0” for
all other respondents.

It was also necessary to modify the travel behavior variables selected for
analyses. The “seasonality” variable was measured using four categories, but was
modiﬁed into a dummy variable, as follows: “1” if respondents took a pleasure trip in
summer, “0” otherwise. The “primary purpose of trip” variable was measured using
seven categories, but was modiﬁed as follows: “1” if the primary purpose of the
respondent’s trip was visiting friends or relatives, “0” otherwise. The “primary mode of
transportation used” variable was measured using 11 categories, but for the purpose of
analysis it was modiﬁed into a dummy variable with the following values: “1” if
respondents used a car or truck, with or without camping equipment, “0” for all other
respondents. The “type of trip” variable was measured by a dichotomous scale, and for

‘61”

the purposes of analysis was recorded as follows: if respondents took an overnight
trip, “0” otherwise. The “main destination of trip” variable was measured by an open-
ended question, which was coded into 50 states. Then respondents who traveled to an
out-of-state destination from their resident state were given a “1,” and all others a “0.”
The “main type of lodging used while traveling” variable was measured using nine

6619’

categories, but was modiﬁed into a dummy variable: for respondents who used a
friend’s or relative’s home as their accommodation, “0” for all other respondents. The
“activity participation at travel destination” and “propensity to search for travel
information online” variables were asked using a dichotomous scale (i.e., yes or no). If

respondents participated in each activity (e.g., outdoor recreation, shopping, and casino

gambling), they were given a “1”; if not, they were given a “0.” Similarly, if respondents

59

used the Internet to obtain travel information, they were given a “1”; if not, they were

given a “0.” A summary of the modiﬁcation of variables is presented in Appendix B.

Data Analytical Methods and Procedures
This section discusses data analytical methods and procedures used in the study.
The rationale for selecting the statistical techniques employed is included under each
section heading. Data were analyzed using the Statistical Package for Social Science

(SPSS) version 12.0 for Windows, 2003.

Frequency and Descriptive Analyses

To provide an overview of the respondents’ sociodemographic proﬁles and their
pleasure trip characteristics over time, frequency and descriptive analyses were
employed. Which statistical tests to apply were determined based on the measurement
scales used to collect the data. If data were collected using a nominal scale, then they
were tested using the Chi square or Phi coefﬁcient test. However, if data were collected
using ratio scales, then they were tested using the independent samples t-test. Chi-square
and Phi coefficient tests were used to test the signiﬁcance of group differences or
correlations when data were reported as nominal scales. The differences between Chi
square and Phi coefﬁcient tests are dependent on the number of categories in each
variable. For instance, if questions were laid out on a 2 X 2 table (i.e., 2 rows and 2
columns), they are tested using the Phi coefﬁcient; however, if questions were on a 3 X 2,
4 X 2, or 5 X 2 table, they were tested using the Chi square. Phi coefﬁcient tests are

suggested to conduct analyses of a 2 X 2 table (Guilford & Fruchter, 1978). An

60

independent samples t-test was applied in this study to determine if the means of the
dependent variables were statistically signiﬁcant with respect to the two years studied

(1997 and 2002).

Sociodemographic Proﬁle of Pleasure Travelers. The sociodemographic proﬁle
of respondents is presented in Table 5. Sociodemographic characteristics of pleasure
travelers in 1997 and 2002 were tested to examine homogeneity between the longitudinal
samples, because it is important to ascertain that respondents of two samples represent
approximately the same population.

A statistically signiﬁcant difference between 1997 and 2002 pleasure travelers
was found to exist with respect to respondents’ state of residence at the .05 level of
probability, {(5) = 18.414, p = .002. The results indicated that the 2002 data set included
more respondents from Indiana, Michigan, and Ontario and fewer from Illinois, Ohio,
and Wisconsin. Furthermore, signiﬁcant differences were found in gross annual
household income between the 1997 and 2002 respondent group at the .05 level of
probability, f(2) = 67.322, p = .000. Respondents’ income decreased between 1997 and
2002. No signiﬁcant differences were found across the other sociodemographic variables
included in Table 5.

Based on the results, it can be concluded that the observed difference in the
sample across the study region is not problematic, but the lower income in the 2002

sample is cause for some concern.

61

 

Table 5. Sociodemographic Proﬁle of Pleasure Travelers

 

 

 

1997 2002
Travelers Travelers Test P

Variables (n=2,516) (n=3 ,219) statistics value
Residence state

Illinois 22.6 20.4 X: = 18.414 .002

Indiana 10.3 11.4

Michigan 16.9 18.1

Ohio 22.4 19.4

Wisconsin 9.8 9.7

Ontario (Canada) 189 _2_1_.0_

Total 100% 100%
Household contained. . .(%, Yes)

Pre—school child(ren) 14.5 12.7 0 = .026 .056

School—age child under age 18 34.2 35.6 D = .014 .287

Senior citizen 19.8 21.4 D = .019 .151

Disabled person(s) 4.9 5.2 O = .007 .616
Household size (mean) 2.9 2.9 t = —.035 .972
Ethnicity

White 91.8 90.3 x2 = 7.494 .278

Hispanic 1.3 1.6

Black 4.1 4.4

Asian or Paciﬁc Islanders 1.4 1.6

Multiracial 0.7 1.1

American Indian or Alaskan Native 0.6 0.6

.th L1 M.

Total 100% 100%
Employment

Full—time 64.6 64.8 x2 = 5.556 .475

Part—time 8.8 7.9

Retired 14.5 15.8

Not employed 2.2 1.8

Homemaker 4.6 4.2

Student 3.8 3.7

Other employment situation Q 1_._8_

Total 100% 100%
Gross annual household income

Less than 842,000 17.7 27.5 f = 67.322 .000

$42,001 -~ $65,000 31.9 28.3

More th_a_n $65,000 13.4 Q

Total 100% 100%

 

Note. The study region was deﬁned as the Canadian province of Ontario and the states of Illinois,
Indiana, Michigan, Ohio, and Wisconsin.

62

Pleasure Trip Characteristics. The pleasure trip characteristics of 1997 and 2002
travelers included in the data set that was used are presented in Table 6. With respect to
the traveling year of respondents, a statistically signiﬁcant difference was found in their
primary purpose of trip at the .05 level of probability, 12(6) = 47.674, p = .000. The
percentage of those who had indicated “visit ﬁiends or relatives” as their trip purpose
decreased in 2002 (27.1% in 2002 vs. 31.4% in 1997). However, the percentage of those
who indicated “vacation/ho]iday/recreation/amusement” as their trip purpose increased in
2002 (33.7% in 2002 vs. 28% in 1997). A statistically signiﬁcant difference was also
found in primary mode of transportation used on their trip at the .05 level of probability,
[(10) = 27.866, p = .002. The percentage of those who used a car or truck without
camping equipment increased in 2002 (68.7% in 2002 vs. 66.6% in 1997). However, the
percentage of those who used an airplane as their primary mode of transportation while
traveling decreased in 2002 (22.8% in 2002 vs. 24.1% in 1997). The hijackings of
September 11, 2001 raised fears about the safety and security of air travel, although
higher plane fares and uncertainty caused by leading carriers ﬁling for bankruptcy in
2002 might also have been factors in this small decrease. Comparing 1997 and 2002

9“

travelers, respondents average age of all persons in travel party” was found to be
statistically different at the .05 level of probability, t = —3.25, p = .001, two-tailed. The
“average age of all persons in travel party” slightly increased in 2002 (40.3 years old in

2002 vs. 38.9 in 1997), probably reﬂecting the aging of the overall population in the

study region.

63

Table 6. Pleasure Trip Characteristics

 

 

 

 

1997 2002
Travelers Travelers Test P

Variables (n=2,5 l 6) (n=3 ,2 19) statistics value
Number of trips taken in past 12 months (mean) 5.3 5.2 t = .160 .873
Season in which trip began

Spring (March through May) 19.8 19.8 x2 = 2.340 .505

Summer (June through August) 36.7 37.9

Fall (September through November) 25.0 25.2

Winter (December throgh Februm) 1_8_.§ 11.1

Total 100% 100%
Primary purpose of trip

Visit friends or relatives 31.4 27.1 12 = 47.674 .000

Vacation/ho]iday/recreation/amusement 28.0 33.7

Relaxation 10.3 12.3

Entertainment 1 l .1 9.6

Outdoor recreation 9.1 7.1

General touring 3.3 4.5

2.0191 _6-_8 .527.

Total 100% 100%
Primary mode of transportation

Car/truck without camping equipment 66.6 68.7 )(2 = 27.866 .002

Car/truck with camping equipment 2.5 3.1

Self—contained recreation vehicle 1.5 0.6

Rental car 0.9 1.0

Airplane 24.1 22.8

Train 1.0 0.7

Ship/boat 0.5 0.7

Motorcycle 0.3 0.4

Bicycle 0.0 0.0

Motorcoach/bus 2.2 2.0

cm. .035 0.0

Total 100% 100%
Trip party size (mean) 3.7 3.5 t = 1.434 .151
Average age of all persons in travel party 38.9 40.3 t = —3.250 .001
% of Parties that included person(s) ageda

Less than 10 34.7 39.2 Not

11 to 20 38.7 37.6 Available

21 to 30 46.9 41.7

31 to 40 60.8 49.8

41 to 50 57.6 56.2

51 to 60 38.3 38.7

61 to 70 20.3 20.9

Above 71 9.3 11.9
Type of trip

Overnight trip 92.4 89.8 0 = .044 .001

Damip L6 M

Total 100% 100%

64

Table 6. (cont‘d)

 

 

1997 2002
Travelers Travelers Test P
Variables (n=2,5 16) (n=3,219) statistics value
Travel destination
In—state 45.8 51.3 D = .054 .000
Out-of—state m 48_.7
Total 100% 100%
Length of trip (mean)
Number of nights away from home 6.9 6.1 t = 2.397 .017
Number of nights spent in the destination 6.2 5.5 t = 2.391 .017
Commercial lodging spending per night $58.68 $65.42 t = —2.873 .004
Main type of lodging used
Friend's/relative's home 25.8 24.4 )(2 = 18.207 .020
Hotel/motel/ lodge 49.8 53.1
Bed & Breakfast 1.7 2.0
Rented cabin/cottage/condominium 8.3 6.2
Owned cabin/cottage/condominium 4.4 5.3
County/state/federal campground 2.1 1 .4
Commercial campground 2.9 3.0
Boat/ship 1.4 1.6
@1191. .326 _3_&
Total 100% 100%
Activities participated in w/traveling (%,Yes)
Nightlife 41.6 42.2 0 = .006 .659
Visit state or national park 33.6 35.1 D = .016 .243
Visit museum or hall of fame 26.4 24.5 D = .021 .110
Visit historic site 39.6 37.7 = .019 .151
Visit some other type of attraction 58.4 56.1 = .022 .093
Explore small city or town 54.3 58.3 = .040 .002
Dine at unique restaurant 58.7 62.1 = .034 .010
Fall color touring 6.1 7.0 = .017 .208
General touring or driving for pleasure 62.0 57.6 = .044 .001
Outdoor recreation 57.2 54.8 = .024 .069
Shopping 63.6 72.7 = .096 .000
Attend a festival or event 29.8 17.8 = .140 .000
Casino gaming 15.0 12.1 = .043 .001
Number of trip activities participated in (mean) 5.4 5.3 t = .628 .530
Average trip expenditures $1,155.98 $1,091.42 t = .900 .368
Average trip expenditures per person per trip $424.57 $388.79 t = 1.623 .105
Median trip expenditures per person per trip $200.00 $175.00
Number of days since began to plan trip (mean) 90.5 78.2 t = 4.083 .000
Access to the Internet (%, Yes) 49.1 82.1 D = .331 .000
Obtained travel information online (%, Yes) 56.3 79.8 D = .236 .000

 

a. Percentages add to more than 100% due to multiple responses.

65

A statistical difference between 1997 and 2002 travelers’ type of trip was also
found at the .05 level of probability, 0 = .044, p = .001, two-tailed. Daytrip travelers
increased in 2002 over 1997 (10.2% vs. 7.6%). People may have preferred to remain
closer to home immediately after September 11, 2001 and/or reduced their travel
spending in keeping with reduced incomes. A signiﬁcant difference was found in travel
destination at the .05 level, 0 = .054, p = .000, two-tailed. The number of in-state
travelers increased in 2002 over 1997 (51.3% vs. 45.8%). There were also signiﬁcant
differences in the number of nights away from home and the number of nights spent at
the destination at the .05 level of probability, t = 2.397, p = .017, two-tailed and t = 2.391,
p = .017, two-tailed, respectively. Those who traveled in 2002 spent fewer nights away
from home or at the destination than did travelers in 1997 (6 nights vs. 7 nights, 5 nights
vs. 6 nights, respectively), again possibly because of reduced incomes.

In terms of accommodations used, signiﬁcant differences were found in
commercial lodging spending per night and type of lodging used at the travel destination
at the .05 level of probability, t = —2.873, p = .004, two-tailed and 12(8) = 18.207, p = .02,
respectively. Respondents in 2002 spent more money on accommodations than did 1997
respondents ($65.42 vs. $58.68). In addition, the percentage lodging in a hotel or motel
increased in the 2002, compared to 1997 (53% vs. 50%). However, those staying with
ﬁiends or relative’s decreased in the 2002, compared to 1997 (24% vs. 26%).

The results for “activities participated in while traveling” indicates that there were
statistical differences at the .05 level of probability for the following activities: “Explore
small city or town,” 0 = .04, p = .002; “Dine at unique restaurant,” 0 = .034, p = .01;

“General touring or driving for pleasure,” O = .044, p = .001; “Shopping,” O = .096, p

66

= .000; “Attend a festival or event,” 0 = .14, p = .000; “Casino gaming,” 0 = .043, p
= .001. Speciﬁcally, more 2002 travelers (58.3%) participated in an “explore small city or
town” activity than 1997 travelers (54.3%); they dined at a unique restaurant (62.1%)
more than 1997 travelers (58.7%); and they shopped more (73%) than 1997 travelers
(64%). In contrast, 2002 travelers participated less in general touring or driving for
pleasure activities (57.6%) than 1997 travelers (62%); they participated less in an “attend
a festival or event” activity (17.8%) than those in 1997 (29.8%); and they participated
less in casino gaming activities (12.1%) than travelers in 1997 (15%).

With respect to trip planning horizon (listed as “Number of days since began to
plan trip” in Table 6), a statistically signiﬁcant difference existed in trip planning
intervals at the .05 level of probability, t = 4.083, p = .000. Travelers in 2002 reported
having a much shorter trip planning interval than those in 1997 (78 days vs. 91 days). In
addition, there were signiﬁcant differences in having access to the Internet and obtaining
travel information online at the .05 level of probability, 0 = .331, p = .000; and 0 = .236,
p = .000, two-tailed, respectively. The vast majority (82.1%) of respondents in 2002
accessed the Internet, compared to just under half (49.1%) of respondents in 1997.
Similarly, the vast majority (79.8%) of 2002 respondents obtained travel information

online, compared to 56% of respondents in 1997.

Independent Samples t-test
To test the relationship between the effect of time of travel and selected travel
behavior variables, independent samples t-tests were conducted. Independent samples t-

tests were used to determine if each mean of the selected travel behavior variables, such

67

9, 6‘

as “travel propensity, trip planning horizon,” “propensity to search for travel
information online,” and “travel expenditures on trips” differs signiﬁcantly between 1997
and 2002 travelers at the .05 level of probability; if the t statistic showed signiﬁcance
between the effect of time of travel and travel propensity, it would indicate that a
statistically signiﬁcant relationship existed over time.

In terms of the homogeneity of variance in the two groups, Levene’s test for
equality of variances (computed by SPSS) was used to verify the assumption that the two
samples have approximately equal variance on the dependent variable (Cramer, 1994).
For example, if the Levene’s test is nonsigniﬁcant (the value is greater than .05), the two
variances are not signiﬁcantly different, which means that the two samples come from an
identical population so that the results of the row “equal variances assumed” (computed
by SPSS) would be reported; however, if the assumption is violated, the results of the
row “equal variances not assumed” (computed by SPSS) would be reported.

Signiﬁcant effects that resulted from the independent samples t-tests were used

for ﬁlrther analysis.

One—way Analysis of Variance

One-way analysis of variance (ANOVA) tested the relationship between the
effects of biological age and generational cohort and selected travel behavior variables.
The reason for applying one-way ANOVA is that it will determine if the means of
selected travel behavior variables differ statistically with respect to 11 age groups and 12
generational cohort groups. If signiﬁcant relationships between the biological age group

variables and selected travel behavior variables are found based upon calculated F

68

statistics, then follow-up analysis of Post hoc tests such as Tukey Honestly Signiﬁcant
Difference (HSD) were also applied to examine all possible combinations to identify
signiﬁcant mean differences among the independent groups. The usual statistical
assumption that the independent groups have population distributions that are normal
with identical standard deviations is not crucial in this case due to the large sample sizes
available for analyses (n = 5,735) (Agresti & Finlay, 1997). Finally, if any effects (i.e.,
biological age and generational cohort) are found to be statistically related to selected
travel behavior variables, those identiﬁed signiﬁcant variables were explored further

using multiple regression analyses or logistic regression analysis.

Multiple Regression

To test the research hypothesis that speciﬁed dominant effect(s) 4 can be
combined with other causal variables to more effectively explain variations in selected
travel behavior—travel propensity, trip planning horizon, and travel expenditures on
trips—multiple regression analysis using the stepwise method was estimated. The main
purposes of this statistical technique were to: (1) identify the underlying predictors that
inﬂuence a dependent variable, (2) build a useful model for predicting the dependent
variable (Wooldridge, 2003), and (3) estimate the variation in the dependent variable
explained by predictors (Cramer, 1994).

To test the research hypothesis and accomplish the main purposes of the study,

semilog models were employed:

 

4 Biological age, time of travel, and generational cohort

69

log y =f(xs),

where,
log y = travel propensity (number of trip taken during 12 months) (model 1)

log y = trip planning horizon (model 2)
log y = travel expenditures on trips (model 3)

X5 = predictors

Model 1, for instance, describes the log of the travel propensity (number of trips
taken during the past 12 months) as a linear function of various predictors (i.e., the effects
of biological age, time of travel, generational cohort, and selected sociodemographic
variables). Similar models have been applied in a variety of social science studies
(Agarwal & Yochum, 1999; Chen, X. et al., 2003; Gujarati, 2004; Wooldridge, 2003).
There are two reasons for transforming original scores into natural logarithm forms in the
models. First, the log transformation accommodates nonlinearities in the multiple
regression models (Gujarati, 2004; Wooldridge, 2003). Second, the logarithm
transformations make it possible to extract directly the percentage changes that predictors
have on the dependent variable.

The regression coefﬁcients were obtained mathematically using the ordinary least
squares’ (OLS) estimation method, which determines the prediction model for which the
sum of squared errors is a minimum (Agresti & Finlay, 1997). In addition, the stepwise
selection technique was employed across the models to identify an underlying set of
predictors that inﬂuence the dependent variable. This technique searches for statistically
signiﬁcant predictors in the model until there are no statistically signiﬁcant predictors leﬁ
(Cramer, 1994; Meyers, Gamst, & Guarino, 2006). The tolerance test was conducted to

detect if multicollinearity, deﬁned as a linear relationship among some or all predictors of

70

a regression model (Gujarati, 2004), exists in the model; if tolerance is less than .20, there
is a problem with multicollinearity.

To assess the goodness-of-ﬁt of a model, R squares were reported. To ﬁnd which
predictor in the multiple regression model would have the greatest impact on the
dependent variable, standardized partial regression coefficients (B) were compared
(Agresti & Finlay, 1997). For predicting the dependent variable, unstandarized regression
coefﬁcients (b) were also compared. As these statistical techniques were performed, the
proposed models would be expected to reveal the casual relationships between dominant
effects (i.e., biological age, time of travel, and generational cohort) and dependent
variables, as well as identify other determinants that explained variations in the

dependent variables.

Logistic Regression

To test the research hypothesis that speciﬁed dominant effect(s) can be combined
with other causal variables to more effectively explain variations in selected travel
behavior—propensity to search for travel information online—logistic regression was
performed, because this statistical technique is used when the response variable has only
two possible outcomes—yes or no (Moore & McCabe, 1996). In the questionnaire,
respondents were asked whether or not they would search for travel information online

(yes or no).

71

A logistic regression model was proposed:
log [TL] = f (XS), (Model 4)
— P

where,

log (p / l — p) = propensity to search for travel information online

XS = predictors

Model 4 describes the log odds as a function of various predictors (i.e., biological
age effect, time of travel effect, generational cohort effect, and sociodemographic
variables). The odds are the ratio of the proportions for the two possible outcomes; when

66 9,

p is the proportion of those who use the Internet to seek travel information, then “1 — p”
is the proportion of those who do not use the Internet to seek travel information. Logistic
regression coefﬁcients were used to estimate odds ratios for each of the independent
variables in the model.

The stepwise selection method was employed to select underlying predictors, with
entry testing based on the signiﬁcance of the score statistic and removal testing based on
the probability of a likelihood-ratio statistic based on conditional parameter estimates.
Cox & Snell R squares and Nagelkerke R squares were computed to assess the goodness-
of-ﬁt of the model.

The next chapter discusses the results of the analysis conducted, possible

explanations of the ﬁndings, and the relevant ﬁndings of previous studies.

72

CHAPTER IV
RESULTS AND DISCUSSION

This study investigated changes in travel behavior patterns linked to biological
age, time of travel, and generational cohort on travel behavior during a ﬁve-year period
(1997—2002), as well as examined other related determinants that inﬂuence travel
behavior. In this study, biological age captures changes in travel behavior due to physical
or life cycle changes which occur along with aging. Time of travel captures changes in
travel behavior inﬂuenced by unique events or signiﬁcant other changes that occur at the
time a trip is taken or is planned (e.g., the ﬂuctuation of gasoline prices, 9/11, economic
situation). Generational cohort captures changes in travel behavior produced by
differences that arise from the unique life experience, history, values, attitudes, and
socialization of cohorts.

The chapter begins with a statement of the study hypotheses. It then reports test
results for hypotheses involving selected travel behavior variables (i.e., travel propensity,
trip planning horizon, propensity to search for travel information online, and travel
expenditures on trips) in relation to biological age, time of travel, and generational cohort.
Signiﬁcant ﬁndings were identiﬁed using one-way analysis of variance (ANOVA) results
and the Independent samples t-test results. Next, results for speciﬁed dominant effects5
and other independent variables that might explain variations in selected travel behavior
variables are reported.

The discussion in this chapter focuses on the most important relationships

between the effects of biological age, time of travel, generational cohort and selected

 

5Biological age, time of travel, and generational cohort.

73

travel behavior variables. The chapter will also explore possible explanations for the

study’s results and compare the results of this study with results from previous studies.

Three Dominant Study Variable Effects on Selected Travel Behavior Variables
In this study, it was hypothesized that a traveler’s biological age, time of travel,
and generational cohort has a signiﬁcant impact on the following travel behavior
variables over time: (1) travel propensity, (2) trip planning horizon, (3) propensity to

search for travel information online, and (4) travel expenditures on trips.

The Effect of Biological Age on Selected Travel Behavior Variables

The hypothesis was tested by using the One-way analysis of variance (ANOVA)
method. Statistically signiﬁcant relationships were found between a traveler’s biological
age and three of the four travel behavior variables selected for this study; the exception
was travel propensity which was not found to be related. Results from analyses of the

relationship between biological age and selected travel behaviors are presented in Table 7.

74

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Travel Propensity. As can be seen in Table 7, a traveler’s biological age was not
found to have a signiﬁcant inﬂuence on travel propensity over time. The association
between biological age and travel propensity was found to be non signiﬁcant at the .05
level, P = 1.515, p = .127. Among age groups, respondents aged 58—62 took the most
pleasure trips (M = 5.8), whereas those aged 68 or older took the fewest (M = 4.7) during
a ﬁve-year period. Generally speaking, people in every age group took approximately
ﬁve pleasure trips over time. This ﬁnding is consistent with the ﬁnding of Oppermann
(1995) but contradictory to the ﬁndings of other studies (Fleischer & Pizam, 2002; Mak
et al., 2005; Mark & Lambert, 2003; Sakai et al., 2000; Warnick, 1993; You & O'Leary,

2000).

Trip Planning Horizon. As indicated in Table 7, younger travelers exhibit much
different trip planning behavior than do older travelers. A strong relationship between
biological age and trip planning horizon (number of days planning began in advance of
the trip) was found at the .05 level, F = 3.890, p = .000. Scanning across the age variable
categories, the tendency for the trip planning horizon to increase with age of respondents
is apparent. Further analysis using the Tukey Honestly Signiﬁcant Difference (HSD) post
hoc test indicated signiﬁcant mean differences in trip planning horizon between the 18—
22 age segment and the 38—42 age segment, between the 18—22 age segment and the 48—
52 age segment, between the 18-22 age segment and the 53—57 age segment, between the
18—22 age segment and the 58—62 age segment, between the 18—22 age segment and the

68 or older age segment, and between the 23—27 age segment and the 68 or older age

76

segment. This ﬁnding is consistent with the ﬁndings of other studies (Rao et al., 1992;
Schul & Crompton, 1983; Yoon, 2000b).

Speciﬁcally, the shortest planning behavior was seen in respondents aged 18—22,
who planned their pleasure trips an average of 60 days in advance, whereas those aged 68
or older began to plan their pleasure trips an average of 94 days in advance. Younger
travelers are most likely to be familiar with and use high-tech resources such as the
Internet, mobile phones, and Global Positioning System (GPS) devices to search for
general travel information (where to eat, what to see, and so on), make a reservation
(airline, lodging, etc.), or ﬁnd instant information while traveling (via GPS devices,
information services from mobile phones). Perhaps because of their use of these high-
tech resources, younger travelers do not appear to be as concerned about planning their
trips ahead of time, compared to older travelers, who are more prone to rely on
conventional methods (e.g., travel agents, printed media, and word-of-mouth referrals) to
plan their pleasure trips (Gitelson & Crompton, 1983; Hansman & Schutjens, 1993). It is
also possible that younger travelers can be more ﬂexible and spontaneous when traveling
because they are less likely to be concerned with needs related to children, pets,

balancing work schedules, home maintenance, health, etc.

Propensity to search for Travel Information Online. As shown in Table 7,
biological age was found to be a signiﬁcant factor in searching for travel information
online at the .05 level of probability, F = 3.867, p = .000. Scanning across age categories,
the tendency for younger travelers to use the Internet to search for travel information is

fairly apparent. Further analysis using the Tukey HSD Post hoc test indicated signiﬁcant

77

mean differences in searching for travel information online between the 18—22 and the 68
or older age groups, between the 23—27 and the 68 or older age groups, between the 28—
32 and the 68 or older age groups, between the 33—37 and the 68 or older age groups,
between the 38—42 and the 68 or older age groups, between the 43—47 and the 68 or older
age groups, and between the 48—52 and the 68 or older age groups. This ﬁnding is
consistent with the ﬁndings of other studies (Bonn et al., 1999; Fodness & Murray, 1997;
Weber & Roehl, 1999).

Younger travelers search for travel information online more than older travelers.
Speciﬁcally, the 33—37 age group used the Internet the most, followed by the 28—32 and
23—27 age groups; the “68 or older” age group used the Internet the least, followed by the
63-67, 58—62, and 53—57 age groups. This ﬁnding is consistent with a report by the
Travel Industry Association of America ("Travelers' Use of the Internet, 2003 Edition,"
2003), indicating that four in ten (40%) of the 18—34 age group used the Internet to obtain
travel information and plan a trip, whereas only roughly two in ten (18%) of those aged
55 or older accessed the Internet to plan a trip and explore travel information. Bonn, Furr,
and Susskind (1999) and Weber and Roehl (1999) also found that biological age is a
signiﬁcant factor differentiating Internet users from non—users. The younger travelers are,
the more they access the Internet to seek travel information and plan a trip online prior to
traveling. Biological age can be a useful indicator to assess Internet use for travel
information.

Based on the study’s “trip planning horizon” and “propensity to search for travel
information online” results, the “trip planning horizon” and “propensity to search for

travel information online” travel behaviors are believed to be closely connected. This is

78

in part because many travelers access the Internet to gather travel information for trip
planning and purchase travel products and services online (Huh et al., 2003; Weber &

Roehl, 1999).

Travel Expenditures. As indicated in Table 7, study results revealed a signiﬁcant
relationship between biological age and travel expenditures at the .05 level, F = 2.467, p
= .000. Scanning across the age categories, the tendency for spending on trips to increase
with age is apparent. Follow-up results of Tukey HSD Post hoc test indicated a
signiﬁcant mean difference in travel expenditure between the 18—22 age group and the 68
or older age group.

1 Speciﬁcally, respondents in the oldest age group (“68 or older”) had the highest
average expenditures, $512.82 per person per trip, whereas those in the youngest age
group (18—22) had the lowest average expenditures, $332.13. This ﬁnding is consistent
with that of Agarwal and Yochum (1999), who found biological age to be a signiﬁcant
determinant of travel expenditures. They contended that the older travelers become, the
more they spend when they travel, in part because as people grow older, their disposable
income typically increases. Conversely, the ﬁndings of previous studies (Lehto et al.,
2004; Thrane, 2002; Weagley & Huh, 2004b) indicated that the older the travelers, the
less they spent at trip destinations. In general, travel expenditures and the income of

travelers are believed to be highly correlated (Agarwal & Yochum, 1999).

79

The Effect of Time of Travel on Selected Travel Behavior Variables

The effect of time of travel on selected travel behavior variables (i.e., travel

propensity, trip planning horizon, propensity to search for travel information online, and

travel expenditures on trips) was tested using the independent samples t-tests. Signiﬁcant

relationships were found between time of travel and trip planning horizon and propensity

to search for travel information online. No relationship was found for travel propensity

and travel expenditures. The test results are summarized in Table 8.

Table 8. The Relationship Between Time of Travel and Selected Travel Behaviors

 

Time of Travel Variable

 

Travel Behavior Variable 1997 2002 value valIue
Travel propensitya 5.3 5.2 .160 .873
Trip planning horizonb 90.5 78.2 4.083 .000“
Search for travel information online0 .563 .798 44.207 .000"
Travel expendituresd $424.57 $388.79 1.623 .105

 

Note. Each number is a mean.

a. Number of pleasure trips taken in past 12 months.
b. Number of days planning began before the trip.
c. Use of the Internet to obtain travel information.
(1. Dollars spent per person per trip.

‘p< .05, ”p< . 01.

Travel Propensity. No relationship was found between time of travel and travel

propensity at the .05 level, t = .160, p = .873, two-tailed. Speciﬁcally, respondents

averaged 5.3 pleasure trips per year in 1997 and 5.2 pleasure trips per year in 2002.

80

Despite the aftermath of the terrorist attacks of September 11, 2001, people took about
the same number of pleasure trips in 2002 as they did in 1997. However, although people
took the same number of trips, their travel behavior patterns changed, perhaps due to
safety concerns. For example, in 2002 more people decided to stay closer to home. They
chose an in-state destination when traveling rather than travel far from home to an out-of-
state destination. This is empirically supported by the fact that over half (51.3%) of the
2002 respondents took their pleasure trips within their states, whereas fewer than half
(45.8%) of the 1997 respondents stayed within their states when traveling (see Table 6,

Chapter III ).

Trip Planning Horizon. “Trip Planning horizon”—the number of days travelers
begin to plan a pleasure trip in advance—got shorter over time; the relationship between
time of travel and trip planning horizon was statistically signiﬁcant at the .05 level, t =
4.083, p = .000, two-tailed. Pleasure travelers in 1997 began their pleasure trip planning
about 91 days prior to traveling, whereas 2002 pleasure travelers started their planning
about 78 days in advance. This ﬁnding is believed to be highly correlated with the
increasing popularity of the Internet and other high-tech devices, which make it easier for
people to obtain travel information quickly as they are planning. They apparently feel less
need to start early. This ﬁnding is believed to be highly correlated with the result of

propensity to search for travel information online.

Propensity to Search for Travel Information Online. The number of people who

obtain travel information online has increased over time. A relationship between time of

81

travel and the propensity to search for travel information online was found to be
statistically signiﬁcant at the .05 level, I = —4.207, p = .000, two-tailed. Pleasure travelers
in 2002 were more likely to use the Internet to obtain travel information than were those
in 1997. Speciﬁcally, the vast majority (79.8%) of 2002 travelers obtained travel
information online, whereas only a little more than half (56.3%) of 1997 travelers did so.
This ﬁnding is in keeping with a report by the Travel Industry Association of America
("Travelers' Use of the Internet, 2003 Edition," 2003) which indicated that the number of
adults who use the Internet in the United States increased from 51 million in 1997 to
more than 113 million in 2002—an increase of 121.6%. Beldona (2005) also found a
strong relationship between time of travel and the search for travel information online
between 1995 and 2000. The tremendous upsurge in Internet usage by those seeking
travel information and planning trips is believed to be due in part to the ever-increasing
amount of travel information available online over time (Huh et al., 2003), as well as the

appearance of economical, user-friendly computers.

Travel Expenditures. Time of travel was found to be an insigniﬁcant factor in
travel expenditures over time. The average travel expenditures per person in the 1997
pleasure travelers were $424.57, whereas those in 2002 were $388.79 per person per trip.
Although there was a slight decrease in travel expenditures during the ﬁve-year period,
no statistically signiﬁcant relationship exists between the time of travel and travel
expenditures variables at the .05 level, t = 1.623, p = .105, two-tailed. Several factors
may have inﬂuenced this result. First, the 2002 sample reported lower household incomes

than did the 1997 sample. Second, an economic recession and the terrorism attacks

82

dampened demand for travel causing suppliers to hold the line of prices. Third, the ready
availability of price information on line hampered suppliers’ ability to increase their

prices.

The Effect of Generational Cohort on Selected Travel Behavior Variables

The effect of generational cohort on selected travel behavior variables (i.e., travel
propensity, trip planning horizon, propensity to search for travel information online, and
travel expenditures) was tested by One-way analysis of variance (ANOVA). Signiﬁcant
statistical relationships were found between generational cohort and all but one of these

travel behavior variables (travel propensity). The results are presented in Table 9.

Travel Propensity. As shown in Table 9, no statistically signiﬁcant relationship
was found between generational cohorts and travel propensity at the .05 level, F = 1.190,
p = .288. The results of the study indicate that all respondents took about 5 pleasure trips
during the past 12 months. The 1940—1944 generational cohort took the most pleasure
trips (M = 5.9), followed by the 1945—1949, 1975—1979, and 1980—1984 cohorts (at 5.6
trips each). Those who were born in 1929 or earlier took the fewest pleasure trips (M =
4.8). This ﬁnding is somewhat contradictory to the ﬁndings of previous studies (Mak et
al., 2005; Mark & Lambert, 2003; Sakai et al., 2000; Warnick, 1993; You & O'Leary,

2000).

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Trip Planning Horizon. As presented in Table 9, statistically signiﬁcant
relationships were found between generational cohorts and trip planning horizon at
the .05 level, F = 4.292, p = .000. Scanning across the generational cohorts, the tendency
for trip planning horizon to decrease with the younger generational cohorts is apparent.
F ollow-up results of Post hoc test (Tukey HSD) indicated signiﬁcance mean differences
between people born before 1929 and from 1970 to 1974, between the prior to 1929
generation cohort and the generational cohorts of the 1975-1979 and the 1980-1984,
between the 1935-1939 generational cohort and the 1980-1984 generational cohort,
between the 1940—1944 generational cohort and the generational cohorts of the
1970-1974, the 1975-1979, and the 1980-1984, between the 1945-1949 generational
cohort and the generational cohorts of 1970-1974, 1975—1979, and 1980-1984.

In general, younger generational cohorts do not plan as many days in advance of
their trips as do older generational cohorts. The youngest study respondents (the 1980—
1984 generational cohort) had the shortest trip planning horizon (M = 57.8 days),
whereas the oldest study respondents (those born in 1929 or earlier) had the longest trip
planning horizon (M = 100.9 days). In between these extremes, the number of planning
days prior to traveling increased more or less gradually from younger generational
cohorts to older generational cohorts. The reason for this ﬁnding may be that younger
generational cohorts (generation X cohort and N generation cohort) grew up with digital
era and are more comfortable with information technology, making them more likely to
use computers, the Internet, and other high-tech resources (e.g., mobile phones, GPS

devices, and so on) unlike older generational cohorts. These high-tech resources can

85

make them less inclined to begin planning trips in advance. This ﬁnding is believed to be

highly associated with the result of propensity to search for travel information online.

Propensity to Search for Travel Information Online. As presented in Table 9,
signiﬁcant statistical relationships were found between generational cohorts and the
propensity to search for travel information online at the .05 level, F = 6.979, p = .000.
Scanning across generational cohorts, the tendency for seeking travel information online
to increase among younger generational cohorts is apparent. Further analysis using the
Tukey HSD Post hoc test indicated signiﬁcant mean differences in the propensity to
search for travel information online between the generational cohort that was born prior
to 1929 and the 1930—1934 generational cohort, between the prior to 1929 generational
cohort and the generational cohorts of 1940-1944, 1945-1949, 1950-1954, 1955-1959,
1960—1964, 1965—1969, 1970-1974, 1975-1979, and 1980—1984, between the
1935-1939 generational cohort and the generational cohorts of 1965-1969, 1975-1979,
and 1980-1984, between the 1940-1944 generational cohort and the generational cohorts
of 1960—1964, 1965-1969, 1975-1979, and 1980-1984.

As expected, younger generational cohorts used the Internet to obtain travel
information more than older generational cohorts. The study’s oldest generational cohort
(people born in 1929 or earlier) had the smallest percentage of people accessing the
Internet to obtain travel information (40.8%); the percentages gradually increased as the
generational cohorts grew younger until the highest percentage of people accessing the
Internet for travel information was reached at the study’s youngest generational cohort

(1980—1984). This ﬁnding is consistent with that of Beldona, Morrison, and Kline (2003),

86

who found that generational cohort was a signiﬁcant factor inﬂuencing whether or not
people searched for travel information online. They contended that younger generational
cohorts adopt new communication technologies faster than older cohorts. “Generation X
cohort (1966—1976)” and “N Generation cohort (1977—?)” have utilized the Internet well

to propensity to search for travel information (Meredith et al., 2002).

T rave! Expenditures. There was a signiﬁcant statistical relationship between
generational cohort and travel expenditures at the .05 level, F = 3.107, p = .000. Scanning
across generational cohorts, the tendency for spending on tap to decrease among younger
generational cohorts is fairly apparent. Further analysis using the Tukey HSD Post hoc
test indicated signiﬁcant mean differences between the 1930—1934 generational cohort
and the generational cohorts of 1950—1954, 1955—1959, 1960—1964, 1965—1969, 1970—
1974, 1975—1979, and 1980—1984.

Older generational cohorts spent more money on travel than younger cohorts.
Respondents in the 1930—1934 generational cohort spent the most while traveling (M =
$626.13), followed by the generational cohorts of 1940—1944, 1929 or earlier, and 1935—
1939. The youngest generational cohort in the study (1980—1984) spent the least on travel
(M = $323.74), followed by the 1970—1974, 1965—1969, and 1975—1979 generational

cohorts.

Summary of Results
The statistical signiﬁcance of the relationships between selected travel behavior

variables and the effects of biological age, time of travel, and generational cohort at

87

the .05 level of signiﬁcance is summarized in Table 10. Overall, biological age, time of
travel, and generational cohort proved to be signiﬁcant factors that inﬂuenced all of the

travel behaviors studied, except for travel propensity.

Table 10. Statistical Signiﬁcance of the Relationship Between Selected Travel
Behaviors and Three Effects

Independent Effect Variables

 

Biological Generational
Travel Behavior Age Time of Travel Cohort
Travel propensity n.s. n.s. n.s.
Trip planning horizon Sig. Sig. Sig.
Search for travel information online Sig. Sig. Sig.
Travel expenditures Sig. n.s. Sig.

 

Note. “Sig.” indicates statistical signiﬁcance at the .05 level with two-tailed test; “n.s.”
indicates no signiﬁcance.

Here are the results for all of the travel behavior variables studied:

0 Travel propensity. No statistically signiﬁcant relationships between travel
propensity and biological age, time of travel, or generational cohort were found.

0 Trip planning horizon. Statistically signiﬁcant relationships were found between
trip planning horizon and all three independent variables (biological age, time of
travel, and generational cohort).

o Propensity to search for travel information online. Statistically signiﬁcant
relationships were found between propensity to search for travel information

online and all three independent variables.

88

0 Travel expenditures. Statistically signiﬁcant relationships were found between
travel expenditures and “biological age” and “generational cohort,” but not “time
of travel.”

The number of trips is steady across biological age, time of travel, and
generational cohort. Trip volume is unlikely to change as population ages. However,
travel expenditures on trips increase with biological age and generational cohort.
Spending on travel is likely to increase as population ages. The shortening of trip
planning horizon may reverse since older people tend to have longer trip planning
horizon; however, wider use of the Internet may mitigate this tendency.

These signiﬁcant independent variables were used as predictors in model testing
to determine whether they could be combined with other independent variables to more
effectively explain the variation in selected travel behavior variables. The following

section discusses the results of the model tests.

89

Fitting Selected Independent Variables to the Dependent Variables

The purpose of this section is to discuss the results of model testing and their
interpretation. The hypotheses that biological age, time of travel, and generational cohort
can be combined with other selected causal variables to more effectively explain
variations in the following travel behavior: (1) travel propensity, (2) trip planning
horizon, (3) propensity to search for travel information online, and (4) travel expenditures
on trips, were examined. Four models were tested using multiple regression or logistic
regression.

The discussion focuses on four dependent variables and their relationships to the
effects of biological age, time of travel, and generational cohort and additional

independent variables.6

Travel Propensity Model

Stepwise multiple regression results from ﬁtting selected independent
sociodemographic variables to the dependent variable (travel propensity) are presented in
Table l 1. In this analysis, three variables (biological age, time of travel, and generational
cohort) were excluded from the model, because previous test results (i.e., One-way
analysis of variance and independent samples t-test) indicated that they did not inﬂuence
travel propensity over time.

Two percent of the variation in “travel propensity” was explained by predictors:

Income

State of residence
Household size
Caucasian

l

 

6 Additional independent variables are shown in each model.

90

Table 11. Stepwise Multiple Regression Results from Fitting Selected
Sociodemographic Variables to Travel Propensity

 

 

Unstandardized Standardized t P
Independent Variable/Predictor Coefﬁcient (b) Coefﬁcient ([3) value value Tolerance
Constant 1 .144 22.486 .000
Above-median ($42,001) incomea .190 .099 6.673 .000 .953
. . . b
Michigan resrdent .163 .088 6.040 .000 .998
Householddsizec —.046 —.081 -5.439 .000 .949
Caucasian .128 .045 3 .092 .002 .992
2
R .023
Adjusted R2 .022
F value 27.406
p value .000

 

Note. “Travel Propensity” (number of pleasure trips taken during past 12 months) was used as a
dependent variable for the regression analysis with stepwise method. Stepwise regression adds the most
statistically signiﬁcant variable (the one with the highest F statistic or lowest p—value) until there are
none left. Tolerance indicates that multicollnearity exists in the model if tolerance is less than .20. The
estimates are in log.

a. l=Above-median ($42,001) income, O=Below-median income.
b. 1=Michigan resident, 0=A1I other.

c. Number of persons living in the household.

(1. 1=Caucasian, 0=All other.

This lack of explanatory power may be because the proposed model needs more
predictors or requires more data (Meyers et al., 2006). This low R2 is not uncommon in

social science studies (Wooldridge, 2003); however, it is possible that if the proposed
model had had more predictors, it would have produced more robust results. Predictors
that excluded from the model via stepwise regression included: pre-school child, school-
age child, senior, handicapper, and employment status. The tolerance test indicates that
no multicollnearity existed among predictor variables.

Comparing standardized partial regression coefﬁcients ([3), the income variable
(“above—median income”) explained the largest portion (10%) of the variation in “travel

propensity,” followed by “Michigan resident” (9%), “household size” (8%), and

91

“Caucasian” (5%). Speciﬁcally, respondents who earned an above-median income
($42,001) took more pleasure trips during the ﬁve-year period than those who earned a
below-median income. Michigan residents took more pleasure trips to any destinations
than residents in the other geographic regions included in the study (i.e., Illinois, Indiana,
Ohio, Wisconsin, and the Canadian province of Ontario). Household size (the number of
people living in the household) negatively inﬂuenced the number of pleasure trips taken.
Caucasian respondents took more pleasure trips than other ethnic groups.

The model forecasts that, other things remaining the same, those who have an
above-median income are estimated to take about one-ﬁfth (19.0%) more pleasure trips
than those who have a below-median income. Likewise, Michigan residents are estimated
to take about one-sixth (16.3%) more pleasure trips than residents in the other geographic
regions. Caucasians are estimated to take about 12.8% more pleasure trips than other
ethnic groups. In terms of a quantitative variable in the model, the model indicates that if
household size increases by 1 person, travel propensity decreases by 4.6%. In other words,
the fewer members that a household had, the more pleasure trips the household took,
other things being equal. This ﬁnding may be related to travel costs, which may constrain
larger households from taking pleasure trips.

In sum, although other external and internal factors also inﬂuence people’s
propensity to travel, income was found to be the most deterministic predictor that
explains travel propensity. A similar ﬁnding is reported by Sakai et al. (2000) and Mak et.

aL(2005)

92

 

Trip Planning Horizon Model

Stepwise multiple regression results from ﬁtting selected independent variables
(biological age, time of travel, generational cohort, and selected sociodemographic and
trip-related variables) to the dependent variable (trip planning horizon) are presented in
Table 12. The research hypothesis, that biological age, time of travel, and generational
cohort can be combined with other independent variables to more effectively explain
variations in the trip planning horizons of travelers, was tested by the model and all three
of these variables were found to be signiﬁcant factors in the model.

Examining the model, 18% of the variation in “trip planning horizon” was

explained by these predictors:

o Out-of-state destination

0 Activity participation

0 Travel propensity

o Travel-party size

0 Car or truck used for primary transportation
0 Lodging at friend’s or relative’s home

0 Travel expenditures

0 Caucasian

0 Biological age effect (18—22)

0 Summer

0 Time of travel (year 2002)

o Generational cohort effect (1970—1974)

0 Household size

93

Table 12. Stepwise Multiple Regression Results from Fitting Independent Variables (Biological
Age, Time of Travel, Generational Cohort, Selected Sociodemographic Variables, and Selected
Trip-Related Variables) to the Dependent Variable (Trip Planning Horizon)

 

 

Standardized

Unstandardized Coefﬁcient t P
Independent Variable/Predictor Coefﬁcient (b) ([3) value value Tolerance
Constant 56.785 3.354 .001
Out-of—state destinationa .594 .200 12.113 .000 .729
Activity participation" .074 .131 8.650 .000 .865
Travel propensityc —.032 —. 127 -8.901 .000 .973
Travel party sized .031 .096 6.586 .000 .945
Car/truck used for primary transportationc —.265 —.081 —4.941 .000 .735
Lodging at friend’s or relative’s home‘ —.271 —.079 —5.426 .000 .939
Travel expendituresg .000 .070 4.495 .000 .822
Whiteh .331 .063 4.391 .000 .973
Biological Age (18 — 22)i —.341 —.057 —3.977 .000 .952
Summerj .145 .047 3.275 .001 .960
Time of Travel (2002)k —.027 —.046 —3.230 .001 .995
Generational cohort (1970 — 1974)‘ —.203 —.039 —2.749 .006 .987
Household size'“ .030 .029 1.970 .049 .933
R2 .176
Adjusted R2 .173
F value 68.038
p value .000

 

Note. "Trip Planning Horizon" (number of days planning began in advance) was used as a dependent
variable for the regression analysis with stepwise method. Stepwise regression adds the most statistically
signiﬁcant variable (the one with the highest F statistic or lowest p—value) until there are none left.
Tolerance indicates that multicollnearity exists in the model if tolerance is less than .20. The estimates are
in log.

a. l=Out-of-state destination, 0=In-state destination.

b. Number of activities participated in while traveling.

c. Number of trips taken in the past 12 months.

d. Number of persons, including the respondent, in his/her immediate travel party.

e. l=Car/truck used for transportation, 0=All other.

f. 1=Lodging at friend’s and relative’s home, 0=All other.

g. Dollars spent per person per trip.

h. 1=White, 2=A|l other.

i. l=Age 18—22, 0=68 or older.

j. 1=Summer, O=All other.

k. 1=Year 2002, 0=Year 1997.

l. l=1970—1974, 0=1980—1984.

m. Number of persons living in the household.

94

Predictors that were excluded from the model via stepwise regression included:
biological age (28—32, 33—37, 38—42, 43—47, 48—52, 53—57, 58—62, 63—67), generational
cohort (1929 or earlier, 1930—1934, 1935—1939, 1940—1945, 1945-1949, 1950—1954,
1955—1959, 1960—1964, 1965—1969, 1975—1979), residence, pre-school child, school-age
child, senior, handicapper, employment, income, visiting friends or relatives, overnight
trip, and lodging expenses. Comparing standardized partial regression coefﬁcient ([3), the
out-of-state destination variable explained the largest portion (20%) of the variation in
trip planning horizon, followed by activity participation (13%), travel propensity (13%),
travel—party size (10%), car/truck used for primary transportation (8%), lodging in
friend’s or relative’s home (8%), travel expenditures (7%), Caucasian (6%), 18—22 age
(6%), summer (5%), time of travel (5%), 1970—1974 cohort (4%), and household size
(3%).

With respect to speciﬁed dominant effects, biological age (age 18—22 group), time
of travel (year 2002), and generation cohort (those who were born between 1970 to 1974)
were identiﬁed as signiﬁcant factors that affected “trip planning horizon” in the model;
respondents who were in the 18—22 age category had shorter trip-planning intervals than
those 68 or older. Pleasure travelers in 2002 also had planning intervals shorter than those
of the 1997 pleasure travelers. In terms of generational cohort, those who were born
between 1970—1974 also had trip-planning intervals shorter than those who were born
from 1980—1984. The youngest age group (ages 18—22) and the middle generational
cohort (those who were born between 1970 to 1974) were found to have shorter trip-

planning intervals. The reasons for these ﬁndings are not clear. While adding both

95

biological age and generational cohort improve the ﬁt of the regression model, the
improvement is small and of little use in a marketing context.

Comparing 1997 pleasure travelers with 2002 travelers, it is evident that trip-
planning intervals grew shorter during the ﬁve-year period. A possible explanation for
this ﬁnding is that the proliferation of technological innovations and services during this
ﬁve-year period, such as the increasing number of opportunities to make ﬂight and
lodging reservations via the Internet, might have inﬂuenced people to start their trip
planning later than they used to. According to a report of the Travel Industry Association
of America ("Travelers' Use of the Internet, 2003 Edition," 2003), those who use the
Internet at home, or at work or school (or both), increased 121.6% from 1997 to 2002; in
actual numbers, Internet users increased from 51 million in 1997 to more than 113
million in 2002. In terms of travel, the number of people who plan their trips online has
multiplied more than four times, from 12 million people in 1997 to 63.9 million in 2002;
the number of people who make travel reservations online has grown from 5.3 million in
1997 to 39 million in 2002. Because of the speed and convenience of the Internet,
travelers today do not need to plan their trips far ahead of time.

Respondents who traveled to out-of—state destinations began to plan their pleasure
trips earlier than those who traveled in-state. The out-of-state destination variable is also
highly correlated with “activity participation” and “travel expenditures” travel behaviors.
Study results indicate that respondents who participated in more activities at their travel
destinations or who spent more money on their trips began to plan their trips earlier than
other travelers. In other words, they had longer trip-planning intervals. A possible

explanation for these ﬁndings is that if people travel farther away from home (i.e., to an

96

out-of-state destination), they tend to spend more time obtaining travel information to
help them plan activities at their travel destinations, and they are more likely to make an
airplane or hotel reservation than those traveling in—state. In addition, those who take a
long-distance (out-of-state) trip and participate in many activities when they reach their
destination will spend more money on transportation (e.g., gasoline, or airplane tickets),
lodging, and activities than those who stay closer to home and do not participate in many
activities when they reach their destination. Gitelson and Crompton (1983) suggested that
a pleasure trip is a high risk purchase due to the high investment of discretionary money
and time; therefore, people spend more time in planning out of state trips in order to
reduce the perceived risk. Finally, those who plan in-state trips are more likely to have
previously visited their destination and need less time to gather information to plan an in-
state trip.

Predictors associated with longer trip-planning intervals included travel-party size,
ethnicity (“Caucasian”), traveling in summer, and household size. Respondents, who had
more people in their travel party, were more likely to be Caucasian, travel in summer,
have more persons in their household and have longer trip-planning intervals than their
counterparts. Conversely, predictors associated with shorter trip-planning intervals
included travel propensity, car/truck for primary transportation while traveling, and
lodging in a ﬁiend or relative’s home. Those who took more pleasure trips in the past
year were found to have shorter trip-planning intervals, probably due to the frequency of
their trips. Respondents who used their car or truck for their primary transportation while
traveling also had shorter trip-planning intervals. U.S. travelers have traditionally used

automobiles as their primary mode of transportation (Huh et al., 2002). Since they do not

97

need to make an airline reservation in advance, this allows them the luxury of shorter
trip-planning intervals. Similarly, those who stayed at a ﬁiends’ or relative’s home at
their travel destination were found to have shorter trip-planning intervals, probably
because they do not have to make a reservation in advance for lodging.

In terms of forecasting trip planning horizon behavior, the model indicates that
pleasure travelers traveling to out-of-state destinations had 59.4% longer trip-planning
intervals than pleasure travelers traveling to in-state destinations; those using a car or
truck for their primary transportation had 26.5% shorter trip-planning intervals than those
using other forms of transportation; those who stayed with a friend or relative had 27.1%
shorter trip-planning intervals than those who did not; Caucasians had 33.1% longer trip-
planning intervals than other ethnic groups; those in the 18—22 age segment had 34.1%
shorter trip-planning intervals than those in the “68 or older” age segment; those traveling
in the summer had 14.5% longer trip-planning intervals than those traveling in the other
three seasons; those traveling in 2002 had 2.7% shorter trip-planning intervals than those
traveling in 1997; and those born from 1970—1974 had 20.3% shorter trip—planning
intervals than those born from 1980—1984.

Results also indicate that increasing the number of activities participated in at the
travel destination by one results in a 7.4% increase in the length of the trip planning
horizon. Increasing the number of trips taken in a year by one decreases the number of
trip-planning days by about 3.2%. Likewise, if travel-party size increases by one person,
the trip-planning interval will increase by 3.1%; if household size increases by one person,
the trip-planning interval increases by 3.0%; and a one hundred dollar increase in travel

expenditures will result in a trip-planning interval increase of 1.0%.

98

 

In sum, examining biological age, time of travel, and generational cohort, a
traveler’s time of travel seems to be an important factor in explaining trip planning
horizon behavior. During the ﬁve-year period studied, the tremendous increase in
technological innovations had a signiﬁcant impact on people’s daily life, including their
trip planning behavior. Comparing the variables examined in the model, the “out-of-state
destination” variable was responsible for the largest portion of the variation in trip
planning horizon. This means that those planning a long-distance trip will have longer

trip-planning intervals.

Propensity to search for Travel Information Online Model

The results of stepwise logistic regression from ﬁtting selected independent
variables (biological age, time of travel, generational cohort, and selected
sociodemographic variables) to the dependent variable (propensity to search for travel
information online) are summarized in Table 13. The dependent variable here is whether
respondents search for travel information online (1=Yes, 0=No). Biological age, time of
travel, and generational cohort were included in this model, because they were found to
be strongly associated with the dependent variable in previous test results. The research
hypothesis, that biological age, time of travel, and generational cohort can be combined
with other independent variables to more effectively explain the variation in “propensity
to search for travel information online,” was tested via this model. The result supported
the research hypothesis.

The predictors in the model explained seven to eleven percent of the variation in

propensity to search for travel information online (pseudo R2 = .069, Cox & Snell R2 =

99

.078, and Nagelkerke R2 = .113, see Table 13). The model correctly predicted 74% of the

cases. The model was found to be strong. Six predictors were identiﬁed in this analysis,

with statistical signiﬁcance at the .05 level:

Time of Travel (the year 2002)
Generational cohort (1 97 5—1 979)
Generational cohort (1935—1939)
Generational cohort (1940—1944)
Generational cohort (1929 or earlier)

Above-median ($40,001) income

Table 13. Stepwise Logistic Regression Results from Fitting Independent Variables
(Biological Age, Time of Travel, Generational Cohort, and Selected Sociodemographic
Variables) to the Dependent Variable (Propensity to search for Travel Information

 

 

Online)

Unstandardized Standard Wald P Odds
Independent Variable/Predictor Coefﬁcient (b) Error Chi-Square df value Ratio
Constant —.1 10 .1 18 .866 l .352 .896
Time oftravela 1.214 .087 194.015 1 .000 3.367
Above-median ($40,001) incomeb .515 .107 23.344 1 .000 1.674
Generational cohortc .340 .163 4.373 1 .037 1.405
Generational cohortd —.551 .212 6.776 1 .009 .577
Generational cohortc —.634 . 160 15.793 1 .000 .531
Generational cohortf —1 .3 78 .235 23.450 1 .000 .252
—2 Log likelihood 3393.752
Pseudo R2 .069
Cox & Snell R2 .078
Nagelkerke R2 .113
Overall percentage .737

 

Note. “Propensity to search for Travel Information Online” was used as a dependent variable for the
multiple logistic regression analysis with stepwise method. The estimates are in log.

a. 1=Year 2002, 0=Year 1997.

b. l=Above-median ($42,001) income, 0=Below-median income.
c. l=1975-l979, 0=1980-l984.

d. 1=1935-1939, 0=1980—1984.

e. l=1940—1944, 0=1980—1984.

f. l= 51929, 0=1980—1984.

100

However, other predictors, including biological age (18—22, 23—27, 28—32, 33—37,
38—42, 43—47, 48—52, 53—57, 58—62, 63—67), generational cohort (1930—1934, 1945—
1949, 1950—1954, 1955—1959, 1960—1964, 1965—1969, 1970-1974), pre—school child,
school—age child, senior, handicapper, and employment were excluded from the model
as a result of stepwise modeling procedures.

In terms of time of travel effect, the odds of a respondent searching for travel

information online are a multiple of 3.367, or 236.7% higher for the 2002 pleasure

travelers than the 1997 pleasure travelers.7 In other words, the odds ratio of those who
used the Internet to obtain travel information in 2002 compared to those who did in 1997
equals 3.367. Stated differently, about 337 respondents searched for travel information
online in 2002 for every 100 who did so in 1997. The reason for this tremendous increase
is that, during this ﬁve-year period, computers became more user-ﬁiendly, which made it
easier for people to use the Internet. The cost of accessing the Internet also went down
during these years, which also increased usage. Another important reason for this ﬁnding
is the considerable growth in the presence of travel information and travel companies on
the Internet (Connolly et al., 1998; Huh et al., 2003). However, searching for travel
information online can be fruitful only if people have the skills to ﬁnd and accurately
assess the information that is available. Thus, this ﬁnding should be considered in light of
the following results.

In relation to the generational cohort effect, the odds for using the Internet to seek

travel information are a multiple of 1.405 or 40.5% higher for those who were born in

 

7 (3.367 — 1) x 100 = 236.7
101

1975—1979 than for those born in 1980—1984.8 In other words, the odds ratio of those
who were born in 1975—1979 to those who were born between 1980—1984 equals 1.405.
Stated differently, about 140 respondents who were born between the years 1974-1978
searched for travel information online for every 100 who did so who were born in 1980—
1984.

Likewise, the odds are a multiple of .577 or 42.3% lower for those who were born

in 1935—1939 than for those born in 1980—1984.9 In other words, the odds ratio of those
who were born between 1935—1939 to those who were born between 1980—1984 equals
to .577. That is, only 57 respondents who were born between 1935—1939 searched for
travel information online for every 100 of those who were born between 1973—1983.

Similar results were found for two other generational cohorts. The odds ratio for
those born between 1940—1944 to those born between 1980—1984 equals .531; that is to
say, only 53 respondents born in 1940—1944 searched for travel information online for
every 100 of those born between 1980—1984. The odds ratio of those born in 1929 or
earlier to those born between 1980—1984 equals .252; that is to say, only 25 respondents
born in 1929 or earlier searched for travel information online for every 100 of those born
between 1980—1984.

The use of the Internet to obtain travel information is highly correlated with the
generational cohort. This is probably due to the fact that younger generational cohorts
were exposed to computers and other high-tech devices at the time when they grew up

than were older generational cohorts. This ﬁnding supports the assumption of Meredith et

 

8 (1.405 .- 1) x 100 = 40.5
9 (1 — 0.577) x 100 = —42.3

102

al. (2002), indicating that N Generation cohort (1977—‘2) grew up with high-speed
computers, the Internet, compact discs, and cellular phones so that they have never
known life without them.

With respect to income, the odds of the propensity to search for travel information
online are a multiple of 1.674 or 67.4% higher for those who earned above-median
income than those who earned below-median income.10 In other words, the odds ratio of
those who earned above-median income compared to those who did not equals 1.674.
That is to say, about 167 respondents who had above-median income searched for travel
information online for every 100 of those who had below-median income. This is
consistent with earlier ﬁndings by Bonn, Furr, and Susskind (1999), and Weber and
Roehl (1999), which suggested that income is one of most inﬂuential factors that
distinguishes those who access the Internet from those who do not.

The “propensity to search for travel information online” model indicates that in
2002 over 121% more people sought travel information online than in 1997; those born
between 1975-1979 were estimated to seek travel information online 34% more often
than those born between 1980—1984; those who were born between 1935-1939 are
estimated to have the propensity to search for travel information online 63% less often
than those born between 1979—1983; those born in 1929 or earlier are estimated to search
for travel information online 138% less often than those born between 1980—1984; and
those who earn above-median income are estimated to search for travel information

online 52% more often than those who earn below-median income.

 

'0 (1.674 — 1) x 100 = 67.4
103

In summary, the effects of time of travel and generational cohort overruled
biological age effect. Time of travel and generational cohort were found to be the most
signiﬁcant factors associated with the propensity to search for travel information online.
The proﬁlsion and diffusion of technological innovations likely are responsible for these
ﬁndings. Time is an essential element in the diffusion process, because all individuals
cannot adopt an innovation in a social system at the same time (Rogers, 1995). Internet
usage and other technology innovations are diffused to travelers over time. In addition,
the generational cohort also plays a signiﬁcant role in diffusing such innovations, because
younger generational cohorts are more likely to adopt new communication technologies

than older generational cohorts.

Travel Expenditures Model

Stepwise multiple regression results from ﬁtting selected independent variables
(biological age, generational cohort, selected sociodemographic variables, and selected
trip-related variables) to the dependent variable (travel expenditures) are presented in
Table 14. Two variables (biological age and generational cohort) identiﬁed by previous
test results were used to test the proposed model. The research hypothesis, that biological
age and generational cohort can be combined with other independent variables to more
effectively explain the variation in travel expenditures, was supported by the model.

Biological age and generational cohort were found to be signiﬁcant factors.

104

Table 14. Stepwise Multiple Regression Results from Fitting Independent Variables
(Biological Age, Generational Cohort, Selected Sociodemographic Variables, and
Selected Trip-Related Variables) to the Dependent Variable (Travel Expenditures)

 

Unstandardized Standardized t P

Independent Variable/Predictor Coefﬁcient (b) Coefﬁcient ([3) value value Tolerance
Constant 4.663 64.403 .000
Activity participationa .111 .236 17.124 .000 .883
Lodging expensesb .003 .235 17.564 .000 .935
Out-of-state destinationc .541 .225 14.993 .000 .743
Car/truck used for transportationd —.543 —.201 —l3.689 .000 .776
Trip planning horizone .001 .091 6.799 .000 .937
Household sizef —.075 —.087 —4.830 .000 .517
Above-median ($40,001) incomeg .213 .073 5.391 .000 .907
School-age childh —.165 —.066 —3.761 .000 .540
Visiting friends or relativesi -.1 10 —.042 —3.138 .002 .934
Handicapperi —.201 —.036 —2.736 .006 .980
Generational cohort effect" .155 .032 2.425 .015 .936
Biological age' —. 1 51 —.03 1 —2.279 .023 .932
Biological age'" .169 .027 2.044 .041 .957
Generational cohortn —. 107 -.026 -1.963 .050 .945
R2 .399
Adjusted R2 .396
F value 169.467

1 value .000

 

Note. “Travel Expenditures (dollar spending per person per trip)” was used as a dependent variable for
the regression analysis with stepwise method. Stepwise regression adds the most statistically signiﬁcant
variable (the one with the highest F statistic or lowest p—value) until there are none left. Tolerance
indicates that multicollnearity exists in the model if tolerance is less than 0.20. The estimates are in log.

a. Number of activities participated in while traveling.

b. Dollars spent for commercial lodging per night.

c. 1=Out-of-state destination, 0=In-state destination.

(I. l=Car/truck used for transportation, 0=All other.

e. Number of days planning began in advance.

f. Number of persons living in household.

g. 1=Above-median ($42,001) income, 0=Below-median income.
h. 1=School-age child under age 18 in the household, 0=Otherwise.
i. 1=Visiting friends or relatives, 0=Al| other.

j. 1=Handicapper in the household, 0=Otherwise.

k. l=1940—l944, 0=1980—l984.

l. l=Age 18—22, 0=68 or older.

m. l=Age 63-67, 0=68 or older.

n. 1=1945—1949, 0=1980—l984.

105

Forty percentage of the variation in travel expenditures was accounted for by the

following independent variables:

0 Activity participation

0 Lodging expenses

0 Out-of-state destination

0 Car/truck used for transportation
0 Trip planning horizon

0 Household size

0 Above-median ($40,001) income
0 School-age child

0 Visiting friends or relatives

0 Handicapper

o Generational cohort (1940—1944)
0 Biological age (18—22)

0 Biological age (63—67)

- Generational cohort (1945—1949)

Predictors that did not enter the model via stepwise regression included:
biological age (23—27, 28—32, 33—37, 38—42, 43—47, 48—52, 53—57, 58—62), generational
cohort (51929, 1930—1934, 1935—1939, 1950—1954, 1955-1959, 1960—1964, 1965—
1969, 1970—1974, 1975—1979), residence, pre-school child, senior, employment, travel
propensity, summer, and overnight trip. No multicollnearity was found by the tolerance
test. Comparing standardized partial regression coefﬁcients ([3), activity participation
explained the largest portion (24%) of the variation in the model, closely followed by
lodging expenses (24%), out-of-state destination (23%), car/truck used for transportation

(20%), trip planning horizon (9%), household size (9%), above-median income (7%),

106

school-age child (7%), visiting friends or relatives (4%), handicapper (4%), generational
cohort (1940—1944, 3%), age (18—22, 3%), age (63—67, 3%), and generational cohort
(1945—1949, 3%).

Generational cohorts (1940—1944 and 1945—1949) were associated with variation
in travel expenditures; those born between 1940—1945 were likely to spend more on
travel than those born between 1980—1984. However, those born between 1945—1949
were likely to spend less on travel than those born between 1980—1984. In terms of
biological age on travel expenditures, those in the 63—67 age bracket spent more on travel
than those 68 or older; however, those in the 18—22 age bracket spent less on travel than
those 68 or older.

Positively inﬂuential predictors associated with travel expenditures included:
activity participation, lodging expenses, out-of-state destination, trip planning horizon,
and above-median income, respondents who participated in more activities at the travel
destination, spent more money for lodging, had longer trip-planning intervals, and earned
above—median incomes. In contrast, negatively inﬂuential predictors included: car/truck
used for transportation, household size, school-age child, visiting ﬁiends or relatives, and
handicapper in the household. Respondents who used a car or truck for their primary
transportation, or had a school-age child under the age of 18 in the household, or
indicated that “visiting friends or relatives” was their primary trip purpose, or had one or
more handicapped individuals in their households were likely to spend less on travel than
those who did not.

From a forecasting perspective, the “travel expenditures” model predicts that

those born in 1940—1944 will have travel expenditures about 16% greater than those born

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in 1980—1984, all things being equal. Those in the 63—67 age bracket will have about
17% more travel expenditures than those 68 or older. However, those in the 18-22 age
bracket are estimated to spend about 15% less on travel than those 68 or older, and those
born in 1945—1949 are predicted to spend about 11% less on travel than those born in
1980—1984.

Moreover, those traveling to out-of-state destinations are estimated to spend 54%
more on travel than those traveling in-state. Those earning above-median incomes are
predicted to spend 23% more on travel than those earning below-median incomes.
However, those using a car or truck for their primary transportation while traveling are
estimated to spend 54% less than those using other types of transportation (airplane, train,
etc.). Those with a school-age child under age 18 in the household are estimated to spend
about 17% less on travel than those who do not. Those visiting their friends or relatives
as their main travel purpose are predicted to spend about 11% less on travel than those
having other purposes for their trips. Those with handicapped people in their household
will spend 20% less on travel than their counterparts.

The “travel expenditures” model indicates that one additional activity participated
in at the travel destination will result in an increase in travel expenditures of 11%. A one
hundred dollar increase in lodging expenses will result in an increase in travel
expenditures of 3%. Likewise, for every additional trip-planning day, travel expenditures
will increase by 0.1%. However, a one-person increase in the household will result in a
travel expenditure decrease of 8%.

In sum, generational cohort and biological age were found to have signiﬁcant

effects on travel expenditures. Speciﬁcally, those in older age segments are likely to

108

spend more money while traveling than those in younger age segments, and older
generational cohorts are also likely to spend more money while traveling than younger

generational cohorts.

109

Summary of Results for the Three Independent Variables of Priority Research

Interest

The central question addressed in this research is: what effect will the aging of the
US. population have on the tourism industry? Three independent variables H ,
hypothesized to be most relevant in addressing this question, were identiﬁed. Biological
age was selected because people’s interest and physical abilities change as they become
older, and the US. population is becoming older. Generational cohort was selected
because what people experience in their youth inﬂuences their consumption behavior
later in life. Finally, the year in which a trip was taken or planned was selected because it
is well established that current events or environmental changes have a signiﬁcant impact
on travel behavior.

Recognizing that a host of additional independent variables inﬂuence travel
behavior, those that were accessible in the data set used in this research were examined in
concert with the three independent variables of priority research interest in multiple
regression and logistic regression models to assess their joint and separate ability to
explain variation in four dependent travel behavior variables. The key results relevant to
the three priority research variables are presented in Table 15 and are discussed below.

No signiﬁcant association was found among the three independent variables of
priority interest and travel propensity. Across all three of these variables, statistically
signiﬁcant relationships were found with trip planning horizon. However, no clear and

overall relationships were identiﬁed by the model.

 

H biological age, time of travel, and generational cohort.

110

Table 15. Summary of Results for Selected Signiﬁcant Effects of Biological Age,
Time of Travel, and Generational Cohort on Dependent Variables

 

 

Independent Variables
Biological Generational
Dependent Variables Age Time of Travel Cohort
Travel propensity NAa NAa NAa
Trip planning horizon Age 18—22 (—) Year 2002 (—) 1970—1974 (—)
Search for travel information N A3 Year 2002 (+) 1975—1979 (+)
online 1935—1939 (—)
1940—1944 (—)
Travel expenditures Age 63—67 (+) N Aa 1940—1944 (+)
Age 18—22 (—) 1945—1949 (—)

 

Note. Parentheses “( )” indicate that there are statistically signiﬁcant positive or negative
differences between the effects and travel behavior.
a. No statistical signiﬁcance was found between the independent variable and the dependent

variable.

Generational cohort and time of travel were found to have an inﬂuence on
travelers’ likelihood to search for travel information online. Three generational cohorts
and time of travel were identiﬁed by the model; younger generational cohorts
(Generation X cohort and N generation cohort) appeared to search for travel information
online more than middle or older generational cohorts. In addition, travelers in 2002 were
more likely to obtain travel information online than travelers in 1997. The failure of
biological age to inﬂuence online search behavior is inconsistent with the ﬁndings for the
generational cohort variable and the conventional wisdom that older people are less

frequent users of the Internet. These results indicate that biological age is becoming less

of a factor in the use of the Internet when planning trips.

111

Biological age and generational cohort were found to inﬂuence travel
expenditures on trips, but time of travel had no inﬂuence. Two age groups and two
generational cohorts were identiﬁed as statistically signiﬁcant; older generational cohorts
tend to spend more money while traveling than younger generational cohorts. However,
younger generational cohorts were found to spend more money than middle generational
cohorts. Travelers in older age groups were likely to spend more money than those in
younger age groups.

In sum, time of travel was conﬁrmed as an important inﬂuence of behavior.
Biological age and generational cohort were also conﬁrmed as relevant predictors of
travel behavior. Results were very similar for both the biological and generational cohort
variables, which would indicate that there is limited advantage to tracking both in
marketing research. However, when considered with other predictor variables, biological
age and generational cohort were found to be of secondary importance to many other
variables commonly employed to segment markets. Multivariate analyses performed
were not as fruitful as expected in identifying marketing exploitable opportunities related
to biological age and generational cohort.

Based upon the results from this study, the effect of aging of the US. population
on the tourism industry is probably not as much as is commonly believed. Biological age
and generational cohort may have become less important predictors of travel behavior
than they were earlier while factors such as time of travel, discretionary income,

technology development, household members’ schedules etc. have gained in importance.

112

CHAPTER V
CONCLUSION

This study investigated changes in patterns of travel behavior over time by testing
the effects of biological age, time of travel, and generational cohort. It also examined
determinants that explained variations in travel behavior via multivariate modeling. The
research hypotheses, that a traveler’s biological age, time of travel, and generational
cohort have a signiﬁcant impact on selected travel behavior variables over time and that
the speciﬁed dominant effects could be combined with other causal variables to explain
variations in the selected travel behavior variables, were partially or fully supported.
Ample evidence was found that links changes in patterns of travel behavior with the
effects of biological age, time of travel, and generational cohort over time. The models
employed identiﬁed key dimensions that effectively explained variations in the selected
travel behavior over time.

The following section discusses the most important ﬁndings of the study. The
implications for marketers and practitioners are provided. The study’s limitations and
recommendations for further study are also presented. The summary of key results is

organized according to selected travel behavior.

Summary of Key Results
Travel Propensity
No signiﬁcant effect was found in travel propensity with respect to biological age,
time of travel, and generational cohort over time. This ﬁnding is consistent with the

ﬁnding of Oppermann (1995) and inconsistent with the ﬁndings of other studies

113

(Goeldner & Ritchie, 2006; Mak et al., 2005; Sakai et al., 2000), indicating biological age,
generational cohort, or both affect travel propensity over time.

However, discretionary income was found to explain the most variation in travel
propensity. This ﬁnding is consistent with prior studies (Goeldner & Ritchie, 2006;
Johnson & Suits, 1983). Discretionary income plays a primary role in travel propensity
over time. Travel propensity is income elastic: the higher the income, the more likely
people are to travel.

The three independent variables of priority research interest are of no value in
predicting propensity to travel. Discretionary income, however, was conﬁrmed as the

dominant independent variable inﬂuencing propensity to travel.

Trip Planning Horizon

Statistically signiﬁcant relationships were found between trip planning horizon
and the three primary study effects over time: biological age, time of travel, and
generational cohort. Younger travelers were found to have shorter travel planning
intervals than older travelers. Those who traveled in 2002 had much shorter travel
planning intervals than did those in 1997. Younger generational cohorts did not plan their
trips as many days in advance as did the older generational cohorts. Younger travelers are
more likely to use a variety of resources, such as searching for travel information and
making reservations online, when planning a pleasure trip (Cetron, DeMicco, & Davies,
2006). However, older travelers tend to use conventional resources, such as travel agents,
printed media, and word-of-mouth referrals (Gitelson & Crompton, 1983; Homeman et

al., 2002). Likewise, younger generational cohorts are more comfortable with new

114

technology devices (e.g., the Internet, GPS). It is also possible that younger travelers can
be more ﬂexible and spontaneous when traveling because they are less likely to be
concerned with needs related to children, pets, work schedules, home maintenance, health,
etc. In addition, the growing popularity of computers and the Internet between 1997 and
2002 has likely shortened travelers’ trip planning horizon.

One of the key determinants of trip planning horizon was travel distance. The
farther people have to travel, the longer in advance they plan their trips. In addition, those
who participated in many activities and spent a great deal of money at the trip destination
were the most likely to plan their trips well ahead of departure. This could be because
expensive and busy trips that are far from home increased people’s perception of risk,
which they reduced through taking more time to plan (Gitelson & Crompton, 1983).

Biological age, time of travel, and generational cohort in combination with trip
distance and travel expenditures are especially important predictors of travelers’ trip

planning horizons.

Propensity to Search for Travel Information Online

Biological age, time of travel, and generational cohort were signiﬁcant factors in
the propensity to search for travel information online over time. Younger travelers,
travelers in 2002, and travelers in younger generational cohorts were the most likely to
search online for travel-related information. This ﬁnding is highly correlated with the
ﬁnding for trip planning horizon. Over a ﬁve-year period (1997 and 2002), rapid
technological innovations, such as personal computers, computer software, and the

Internet, have changed every aspect of daily life, including travel behavior. Younger

115

generational cohorts, such as Generation X cohort (1966—1976) and N generation cohort
(1977—?), were the more likely to rely on the Internet and high-tech innovations for travel
information (Meredith et al., 2002).

The variables, generational cohort, time of travel, and discretionary income,
effectively explain variation in propensity to seek travel information online over time.
Generational cohort is the most important factor in propensity to seek travel information
online, followed by time of travel and discretionary income. Possible explanations for
this ﬁnding are that younger generational cohorts (generation X cohort and N generation
cohort) are comfortable with technology because they grew up in digital era. However,
the Depression cohort (1912—1921) and the World War II cohort (1922—1927) have some
difﬁculty with new and unfamiliar technology (Cetron et al., 2006; Meredith et al., 2002;
Russell, 2005). The profusion and diffusion of technological innovations are likely
responsible for these ﬁndings. Time is an essential element in the diffusion process,
because all individuals cannot adopt the innovation at the same time (Rogers, 1995).
Internet usage and other technology innovations are gradually diffused to travelers.
Generational cohort membership plays a signiﬁcant role in diffusing such innovations,
because younger generational cohorts are the most likely to adopt new communication
technologies than are older generational cohorts.

Likewise, discretionary income signiﬁcantly affects the propensity to search for
travel information online: the higher the income, the higher the occupation status, and the
more years of experience with the lntemet, the more likely users are to search for travel

information online or to purchase travel products or services online (Weber & Roehl,

1999).

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Time of travel and generational cohort in combination with discretionary income

are key determinants of travelers’ propensity to acquire travel information online.

Travel Expenditures

Signiﬁcant relationships were found between travel expenditures and the effects
of biological age and generational cohort. Travelers who were 68 or older spent the most
on travel. This ﬁnding supports the ﬁnding of Agarwal and Yochum (1999) and
contradicts the ﬁndings of Thrane (2002), Lehto et a1. (2004), and Weagley and Huh
(2004b). Similarly, generational cohorts, Postwar cohort (1928-1945) and Leading-edge
Baby Boomer cohort (1946—1954), also spent more on travel than any other generational
cohorts.

Further analysis identiﬁed the most inﬂuential factors, older travelers (63 to 67)
and the older generational cohort (Postwar cohort), that explained variation in travel
expenditures. Factors that were the most likely to affect pleasure travelers’ decisions to
spend more on travel expenditures were activity participation, lodging expenses, travel
distance, discretionary income, and trip planning horizon. Similar results were found in a
study by Leones, Colby, and Crandall (1998).

Biological age and generational cohort in combination with activity participation,
lodging expenses, travel distance, income, and trip planning horizon are salient predictors

of travelers’ expenditures on trips.

117

Implications

This study investigated changes in patterns of travel behavior with respect to the
effects of biological age, time of travel, and generational cohort over time. This section
discusses practical, theoretical, and methodological implications of the study.

First, although biological age, time of travel, and generational cohort had no
inﬂuence on travel propensity over time, discretionary income was found to play the most
signiﬁcant role in the number of pleasure trips that people took. Goeldner and Ritchie
(2006) state that “as per capital real incomes continue to rise, consumers should spend an
increasing proportion of their incomes on travel” (p. 305). Thus, a traveler’s discretionary
income can be used as an effective indicator to forecast travel propensity.

Second, travelers’ trip planning horizons have grown shorter over time, in part
because the greater number of opportunities to make ﬂight and lodging reservations via
the Internet might have persuaded them to delay their trip planning. Intensive target times
(approximately two months) of tourism advertising and promotion should be considered
before the tourism season begins. The ﬁndings of this study indicate that early tourism
advertisements and promotions may not draw target audiences’ attention because people
have short trip planning horizons. This study found that travelers start planning their trips
approximately 60 days before they leave. In addition, prior to the beginning of the travel
season, different times to launch tourism advertising programs and promotional messages
are suggested for different geographical markets because pleasure travelers who come a
long distance have longer trip-planning intervals than those who come from a short

distance.

118

Third, the study provided strong evidence of generational cohort and time of
travel impacts on propensity to search for travel information online. Generational cohort
can be one of the key factors in online information searching behavior. For instance,
generation X cohort (1966-1976) and N generation cohort (1977-?) came of age with
personal computers and the Internet. People in these generational cohorts cannot imagine
living without the Internet, GPS, and mobile phones. Destination marketers and
practitioners should use these intermediaries as distributional channels and
communication and promotion tools to differentiate, customize, and personalize their
products and services. Speciﬁcally, Generation X cohort and N generation cohort must be
reached through the Internet and other high-tech devices for marketing purposes. The
generational cohort can likewise be used in generational cohort marketing and Internet
market segmentation strategies.

Fourth, time of travel can explain travel information search online behavior over
time. All individuals cannot adopt the same innovation at the same time. Time is an
essential element in the diffusion of innovation process (Rogers, 1995). Consistent
monitoring of usage rates, penetration of the Internet, and technological trends is
recommended, because Generation X cohort and N generation cohort, for instance,
readily adopt and use them, and change their preferences of technology innovations. If
tourism destination marketers and practitioners keep track of Internet and technology use,
they will be in a better position to reach their targets in the future.

Fifth, older age groups or older generational cohorts have more purchasing power
than younger age groups or younger generational cohorts. Senior travelers are not a

homogeneous market (Bone, 1991; Homeman et al., 2002; Shoemaker, 1989, 2000). If

119

marketing programs can be designed speciﬁcally to attract and serve them, they can
constitute a viable niche market that can reap ﬁnancial rewards. Moreover, it may be
useful to segment senior travelers by their generational cohort membership and design
different appeals to attract them. For instance, those in Post War cohort (1928-1945) may
be susceptible to appeals to nostalgia, opportunities to make friends their own
generational cohort, and availability of recreation (e.g., casino, festivals, and museums).
Finally, very few studies in the tourism and hospitality literature have used
longitudinal data sets. This study successfully demonstrated how existing data can inform
marketing strategies and promotions. The cohort analysis approach has the potential to
change the simplistic manner in which age has been used as a causal variable in travel

marketing research.

Study Limitations

There are some limitations to the ﬁndings of this study. First, the data sets
available in this cohort analysis were limited to two periods (1997 and 2002) with a ﬁve-
year time span. This might have caused the multivariate analyses performed not to be as
fruitﬁll as expected in identifying marketing exploitable opportunities related to
biological age and generational cohort. Data collected over a longer period of time (e.g.,
1987, 1992, 1997, and 2002) could have provided more robust results of the effects of
biological age, time of travel, and generational cohort on travel behavior over time.

Second, some models identiﬁed in the study had somewhat low R squares, which
is not uncommon in individual behavior studies (Berk, 2004; Wooldridge, 2003),

however, the lack of explanatory power may be in part because the proposed model needs

120

more predictors. Thus, it is possible that if the proposed model had had more predictors,
it would have produced more robust results. While an extensive effort was made to
identify the best ﬁtting models for testing this study’s hypothesis, it is possible that better
models may exist and could surface in the future research.

Third, although the MTMS survey achieved a reasonable response rate (44%)
throughout the survey years (Babbie, 2004), increasing cellular phone use and Caller ID
devices may have created a sampling bias for the survey, which was not detected or
obvious in the MTMS survey.

Finally, the sample consisted of residents of the states of Illinois, Ohio, Michigan,
Indiana, and Wisconsin in the United States and the province of Ontario in Canada. Thus,
generalizations to other geographic region of the ﬁndings of this study should be

undertaken with caution.

Recommendations for Further Research

This study examined the effects of biological age, time of travel, and generational
cohort on travel behavior over time. Future research should extend this study to different
travel behavioral variables (e.g., activities, race/ethnicity, trip purposes, purchasing
tourism products and services online, etc.), in order to identify new or different tourism
target markets that may be inﬂuenced by the effects of biological age, time of travel, and
generational cohort over time.

Second, further research should identify what barriers and difﬁculties older
generational cohorts (e.g., World War II cohort, Postwar cohort) may encounter when

searching for travel information online. Although they have more purchasing power than

121

younger generational cohort, they are also less likely to use the Internet to seek travel
information. Reducing impediments to utilizing the Internet via, for example, more user
friendly websites can help in developing more effective Internet marketing strategies.

Third, the study demonstrates that the cohort analysis approach can be utilized to
track changes in patterns of travel behavior over time. Future research should focus on
developing a core questionnaire pertaining to critical travel behavior questions, which
should be collected at two or more intervals. In addition, future surveys in the core
questionnaire should ask respondents their birth year or age as an open-ended question,
thus allowing researchers to compute the age variable in several ways. For example, if the
survey were to collect age data as a ratio scale at two or more points in time, then the age
data could be analyzed to suite researchers’ interests (e.g., a ﬁve-year interval for
generational cohorts and age groups).

Fourth, future research should focus on collecting and using the appropriate time
information to assess the inﬂuence of the time of travel variable. This is because when
respondents are interviewed and when their trip was taken may not be the same time. For
instance, in a telephone survey, a respondent interviewed on May 9, 2002 and asked
when the pleasure trip was taken during the past 12 months may say that it was on May
24, which must be assumed to have been taken on May 24, 2001. But, arriving at this
conclusion requires careful review and recoding of data which is very time consuming.
One solution for this problem is to ask respondents the exact month, day, and year they
took their pleasure trips. In addition, if an existing data set for the cohort analysis is used

for a study, as demonstrated at data selection procedures in Chapter III, substantive

122

subsamples that meet the criteria of biological age, time of travel, and generational cohort,
should be ﬁltered before the data are analyzed.

Finally, longitudinal studies are not common in tourism and hospitality studies.
Based upon the results from this study, time of travel is conﬁrmed as an important
inﬂuence of travel behavior. A priori, marketers can not predict what changes will occur
and how they might inﬂuence travel behavior. Time of travel and its inﬂuence only
become apparent after the fact as was the case in the longitudinal data set used in this
study. Thus, it is strongly recommended for marketers to invest in longitudinal market
research speciﬁc to their markets. Such investments will reveal behavior changes as they
are occurring rather than a year or years later. While historical data can be used in models
to predict possible changes in travel behavior over time with some degree of success as
was demonstrated in this study, such models are unlikely to be good substitutes for
longitudinal research focused on one’s market given how quickly technology and other

changes are occurring and inﬂuencing peoples’ travel behavior.

123

APPENDICES

124

APPENDIX A

Core Questionnaire during a Five-year Period: 1997 and 2002

Gender
1=Male 2=Female -99=can’t determine

Now we’d like to ask you about pleasure trips that you may have taken. Again, we’re
deﬁning “pleasure trips” as any overnight or day trips to places at least 50 miles from
your home that were made for your enjoyment, including vacations, weekend getaways,
shopping trips, and trips to visit friends or relatives.

1. In the past 12 months, have you taken any pleasure trips to any destination? > __

1=Yes
2=No ------------ > GO TO QUESTION 26
~99=DK/NR ----> GO TO QUESTION 26

2. About how many pleasure trips have you taken in the past 12 months?
> _ pleasure trips

[IF RESPONDENT IS UNABLE TO GIVE A SPECIFIC NUMBER, PROBE]
In the past 12 months, would you say you’ve taken ......

2=l to 3 pleasure trips?

5=4 to 6 pleasure trips?

8=7 to 9 pleasure trips?

15=10 to 20 pleasure trips?

25=More than 20 pleasure trips?

-99=DK/NR

BEGIN MOST RECENT PLEASURE TRIP PROFILE BLOCK

3. Approximately when did this trip begin -- the month and day?

MONTH CODES

1=January 4=April 7=July 10=October
2=February 5=May 8=August 1 1=November
3=March 6=June 9=September 12=December
MONTH > __ DAY > _ -99=DK/NR

What was the purpose or purposes of this trip?

 

 

4.
>
>
>

 

125

5. What would you say was the primary purpose of this trip?
>

 

6. What types of transportation did you use? > __

1=Car/truck without camping equipment
2=Car/truck with camping equipment
3=Self-contained recreation vehicle
4=Rental car

5=Airplane

6=Train

7=Ship/boat

8=Motorcycle

9=Bicycle

1 0=Motorcoach/Bus

11=Other

-99=DK/NR

7. Other
>

 

8. How many persons, including yourself, were in your immediate travel party? >

9. Beginning with yourself, please give me the gender and age of each person
who went on this trip:

1=MALE 2=FEMALE -55=REFUSED -99=DK/NR

GENDER AGE GENDER AGE
RESPONDENT > __ > PERSON #2 > _ >
PERSON #3 > _ > PERSON #4 > _ >
> PERSON #6 > _ >
> >
> >

PERSON #5 > _ _ _
PERSON #7 > _ __ PERSON #8 > _ _
PERSON #9 > PERSON #10 > __

10. Was this an overnight or day trip? > __
1=Ovemight
=Day trip ------ > GO TO QUESTION 15
-99=DK/NR ----> GO TO QUESTION 15

11. How many nights were you away from home? > __ -99=DK/NR

12. How many nights did you spend in the state or province containing the
main destination of this trip? > __ -99=DK/NR

126

13. In which locations did you spend these nights?

>

VVVV

14. What was the main type of lodging you used? > __

 

 

 

 

 

1=Friend or relative’s home

2=Hotel, motel, or lodge

3=Bed & Breakfast

4=Rented cabin, cottage, or condominium
5=Owned cabin, cottage, or condominium
6=County, state, or federal campground
7=Commercial campground (e.g., KOA)
8=Boat/ship

9=Other

15. Which, if any, of the following activities did you participate in?

1 =Yes 2=No -99=DK/NR

Nightlife? .................................................

Visit a state or national park? .....................

Visit a museum or hall of fame? .................

Visit an historic site? .................................

Visit some other type of attraction? .............

Explore a small city or town? ......................

Dine at a unique restaurant? ........................

Fall color touring outside of traveling to and from your destination?
General touring or driving for pleasure? .....

Visit a farmer's market or pick-your—own farm 'or orchard?
Outdoor recreation? .................................... .

16. What outdoor recreation activities did you participate in?

>

VVVV

-99=DK/NR

VVVVVVVVVVV

 

 

 

 

 

17. Did you do any shopping on this trip? > _

1 =Yes
2=No -99=DK/NR

127

18. Did you attend a festival or event on this trip? > _

1 =Yes
2=No
-99=DK/NR

19. Did you do any casino gaming on this trip? > _
1=Yes

2=No ------------ > GO TO QUESTION 22
-99=DK/NR ----> GO TO QUESTION 22

20. Did you plan to participate in casino gaming before you leﬁ home on this trip? > _

1=Yes
2=No ------------- > GO TO QUESTION 22
-99=DK/NR ----> GO TO QUESTION 22

21. Was casino gaming the only reason for this trip, a primary reason for
this trip, or a secondary reason for this trip? > _

1=Only 2=Primary 3=Secondary -99=DK/NR
22. What would be your best estimate of how much your immediate travel party
spent altogether while in the state or province containing the main

destination of this trip? > $ -99=DK/NR

23. About how far in advance of this trip did you begin to make plans for it?
>

 

END MOST RECENT PLEASURE TRIP PROFILE BLOCK

24. What was the main destination of this trip?

City/Place:>
State/Province/Country: >

 

 

[DON'T READ; DOUBLE ENTRY REQUIRED]

1=Michigan destination ----—> GO TO QUESTION 52
2=Non-Michigan destination

128

 

 

25. Was a place in Michigan the main destination of any of the pleasure trips
you’ve taken in the past 12 months? > __

1=Yes ----> GO TO QUESTION 27
2=No
-99=DK/NR

26. Have you ever taken a pleasure trip to a place in Michigan? > __

1=Yes
2=No ----> GO TO QUESTION 51

BEGIN GENERAL MICHIGAN PLEASURE TRIP PROFILE BLOCK

27. Now I'd like to ask you about your most recent pleasure trip in Michigan.
Approximately when did this trip begin -- the month and day?

 

 

MONTH CODES
1=January 4=April 7=July 10=October
2=February 5=May 8=August 11=November
3=March 6=June 9=September 12=December
MONTH > _ DAY > __ -99=DK/NR

28. What was the purpose or purposes of this trip?

>

>

>

 

29. What would you say was the primary purpose of this trip?
>

 

30. What types of transportation did you use? > _

1=Car/truck without camping equipment
2=Car/truck with camping equipment
3=Self-contained recreation vehicle
4=Rental car

5=Airplane

6=Train

7=Ship or boat

8=Motorcycle

9=Bicycle

1 0=Motorcoach/Bus

11=Other ----> ENTER UNDER QUESTION 31
-99=DK/NR

129

3 1 . Other
>

 

32. How many persons, including yourself, were in your immediate travel party? >

33. Beginning with yourself, please give me the gender and age of each person who went
on this trip:

1=MALE 2=FEMALE -55=REFUSED -99=DK/NR
GENDER AGE GENDER AGE
RESPONDENT > _ > _ PERSON #2 > _ > _
PERSON #3 > _ > __ PERSON #4 > _ > _
PERSON #5 > __ > __ PERSON #6 > __ > __
PERSON #7 > _ > _ PERSON #8 > __ > __
PERSON #9 > __ > PERSON #10 > _ >

34. Was this an overnight or day trip? > __

1=Ovemight
2=Day trip ------ > GO TO QUESTION 39
-99=DK/NR ----> GO TO QUESTION 39

 

 

 

 

35. How many nights were you away from home? > _ -99=DK/NR
36. How many nights were spent in Michigan? > __ -99=DK/NR
37. In which locations in Michigan did you spend these nights?

>

>

>

>

>

 

38. What was the main type of lodging you used? > __

1=Friend’s or relative’s home

2=Hotel, motel, or lodge

3=Bed & Breakfast

4=Rented cabin, cottage, or condominium
5=Owned cabin, cottage, or condominium
6=County, state, or federal campground
7=Commercia1 campground (e.g., KOA)
8=Boat/ship

9=Other

-99=DK/NR

130

 

39. Which, if any, of the following activities did you participate in?
1=Yes 2=No -99=DK/NR

Nightlife? .....................................................

Visit a state or national park?

Visit a museum or hall of fame? .....................

Visit an historic site?

Visit some other type of attraction?

Explore a small city or town? .........................

Dine at a unique restaurant? ...........................

Fall color touring outside of traveling to and from your destination?
General touring or driving for pleasure? .........

Visit a farmer's market or pick-your-own farm or orchard?
Outdoor recreation? .......................................

VVVVVVVVVVV

 

40. What outdoor recreation activities did you participate in while you were in Michigan?
>

 

 

 

 

VVVV

 

41. Did you do any shopping on this trip? > _
1=Yes

2=No
-99=DK/NR

42. Did you attend a festival or event on this trip? > _
1=Yes
2=No
-99=DK/NR

43. Did you do any casino gaming on this trip? > _
1=Yes

2=No ----------- > GO TO QUESTION 46
-99=DK/NR----> GO TO QUESTION 46

131

44. Did you plan to participate in casino gaming before you leﬁ home on
this trip? > _

1=Yes

2=No -------- > GO TO QUESTION 46
-99=DK/NR----> GO TO QUESTION 46

45. Was casino gaming the only reason for this trip, a primary reason for
this trip, or a secondary reason for this trip? > _

1=Only 2=Primary 3=Secondary -99=DK/NR

46. What would be your best estimate of how much your immediate travel party spent
altogether on this trip while in Michigan? > $ -99=DK/NR

47. About how far in advance of this trip did you begin to make plans for it?
>

 

48. What was the main destination of this trip?

City/Place in Michigan: >

 

END GENERAL MICHIGAN PLEASURE TRIP PROFILE BLOCK
BEGIN MICHIGAN PLEASURE TRIP HISTORY BLOCK

49. Was this most recent pleasure trip in Michigan the ﬁrst pleasure trip
you’ve ever taken in this state? > _

1=Yes ----> GO TO QUESTION 51
2=No
-99=DK/NR

50. About how many pleasure trips to places in Michigan have you taken in
the past 12 months? > _ pleasure trips

[IF RESPONDENT IS UNABLE TO GIVE A SPECIFIC NUMBER, PROBE:]
In the past 12 months, would you say that you’ve taken ......

2=1 to 3 pleasure trips?

5=4 to 6 pleasure trips?

8=7 to 9 pleasure trips?

15=10 to 20 pleasure trips?

25=More than 20 pleasure trips?

-99=DK/NR

END MICHIGAN PLEASURE TRIP HISTORY BLOCK

132

BEGIN MICHIGAN TRAVEL EXPECTATIONS BLOCK

51. During the next 12 months, do you plan to take any pleasure trips to
places in Michigan? > _

1=Yes
2=No
-99=DK/NR
52. Compared to the preceding 12 months, during the next 12 months do
you expect to take more, fewer, or about the same number of pleasure
trips in Michigan? > _
1=More 2=Fewer 3=Same -99=DK/NR
53. Do you plan to take any pleasure trips in Michigan. . .
1=Yes 2=No -99=DK/NR

This fall? > _
How about this Thanksgiving? > __

END MICHIGAN TRAVEL EXPECTATIONS BLOCK
BEGIN PERSONAL/HOUSEHOLD CHARACTERISTICS BLOCK
54. To conclude, we’d like to ask just a few questions to help us classify your answers.

In what city do you live? >

 

55. And your state or province? >

 

56. And your zip or postal code? >

 

57. In what county do you live? >

 

58. Do any of the following types of persons live in your household?

1=Yes 2=No -55=Refused -99=DK/NR
Pre-school child? > _

School-age child under age 18? > _

Senior citizen? > __

Handicapped person? > __

59. How many persons, including yourself, live in your household? > _

133

60. How many adults over age 17, including yourself, live in this household? > _

61. How many full-time wage-earners live in your household? > _
-55=Refused -99=DK/NR

62. Are you ...... > __

1=Employed full-time; 5=A homemaker;
2=Employed part-time; 6=A student; or
3=Retired; 7=In some other employment Situation?
=Not employed; -55=Refused
~99=DK/NR

63. What racial or ethnic group do you belong to?
>

 

~55=Refused -99=DK/NR

64. The median household income is $31,000. Would you say your total
household income before taxes in 1996 was above or below the median? > __

l=Above the median

2=Below the median > GO TO QUESTION 66
-55=Refused [DO NOT READ] ----> GO TO QUESTION 66‘
-99=DK/NR ----> GO TO QUESTION 66

 

65. Was your total household income above $50,000? > _
1=Yes 2=No -55=Refused -99=DK/NR

66. Do you or any member of your household have access to the Internet? > _
1=Yes 2=No -55=Refused -99=DK/NR

67. During the past 12 months, have you or any member of your household used
the lntemet to obtain travel information? > __

1=Yes 2=No -55=Refused -99=DK/NR
END PERSONAL/HOUSEHOLD CHARACTERISTICS BLOCK

68. That’s all the questions I have. Thank you very much for your time!
Have a good evening! [TO TERMINATE, HIT THE ENTER KEY TWICE]

134

APPENDIX B

Type, Operational Definition, Original Coding, and Modiﬁcation of Variables

 

Variable Type

Biological Age

18-22
23-27
28-32
33-37
38-42
43-47
48-52
53-57
58-62
63-67

Time of Travel

2002

GenerationiCohort

S1929

1930-1934
1935-1939
1940-1944
1945-1949
1950-1954
1955-1959
1960-1964
1965-1969
1970-1974
1975-1979
l980-l984

Deﬁnition

Respondents’ age

Respondents’ year
of pleasure trip
taken

Respondents’ birth
year

Original Coding

10=63-67
11=68 or older

1 =1 997
2=2002

1=Sl929
2=l930-l934
3=l935-l939
4=l940-1944
5=1945-1949
6=l950-l954
7=l955-1959
8=l960-1964
9=l965-l969
10=l970-l974
ll=l975-1979
12=l980-1984

135

Modiﬁcation

1=l8—22, 0=68 or older
l=23-27, 0=68 or older
1=28-33, 0=68 or older
1=33-37, 0=68 or older
l=38-42, 0=68 or older
l=43-47, 0=68 or older
l=48-52, 0=68 or older
l=53-57, 0=68 or older
1=58—62, 0=68 or older
l=63-67, 0=68 or older

l=2002
0= 1 997

l=Sl929, 0=1980-1984

l=l930-1934, 0=1980-l984
l=1935-l939, 0=1980-1984
l=1940-l944, 0=1980-l984
l=1945-l949, 0=1980-1984
l=1950-l954, 0=1980-1984
l=1955—l959, 0=1980-l984
l=l960-l964, 0=1980-l984
l=l965-l969, 0=1980-1984
l=1970-l974, 0=1980-l984
l=1975-l979, 0=1980-l984

 

 

 

Variable Type Deﬁnition (Minal Coding Modiﬁcation
Socioeconomic Variables
Michigan residence State residence l=lllinois 1=Michigan residence
2=lndiana O=All other
3=Michigan
5=Ohio
6=Wisconsin
7=Ontario
Pre-school child Pre-school 1=Yes, 2=No 1=Pre-school child(ren)
child(ren) in the 0=Otherwise
household
School-age child School-age child 1=Yes, 2=No 1=School-age child
under age 18 in the 0=Otherwise
household
Senior Senior citizen in the 1=Yes, 2=No 1=Senior citizen
household 0=Otherwise
Handicapper Handicapper in the 1=Yes, 2=No 1=Handicapper
household 0=Otherwise
Household size Number of persons Ratio
living in the
household
Employment Employment status 1=Employed full-time 1=Employed full-time; part-
2=Employ part-time time; some other
3=Retired employment situation
4=Not employed 0=All other
5=A homemaker
6=A student
7=In some other
employment situation
Caucasian Ethnicity l=American Indian or 1=Caucasian
Alaskan native, 0=AII other
2=Asian or Paciﬁc
Islanders,
3=Black,4=Hispanic,
5=White, 13=Other
Income Gross annual 1=Less than $42,000, l=Above median (more than

household income

2=$42,001-$65,000,
3=More than $65,000

136

$42,001 income)
0= Below median (less than
$42,000 income)

 

Variable Type

Deﬁnition

Travel Behayior Variables

Travel propensity

Summer

Visiting friends or
relatives: VFR

Car/truck used for
transportation

Travel party size

Overnight trip

Out-of-state
destination

Lodging expenses

Number of pleasure
trip taken in past 12
months

Season in which trip
began

Primary purpose of
trip

Primary mode of
transportation used

Number of persons,
including a
respondent, were in
his/her immediate

trip party
Type of trip taken

Travel destination
Dollar spending for

commercial lodging
per night

Original Coding

Ratio

l=Spﬁng
2=Summer
3=Fall
4=Winter

1=Outdoor Recreation

2=Entertainment
3=Visiting friends or
relatives
4=Relaxation
5=General Touring

6=Vacation, Holiday,
recreation, Amusement,

Pleasure
7=Other

l=Car/truck without
camping equipment
2=Car/truck with
camping equipment
3=Self-contained
recreation vehicle
4=Rental car
5=Airplane

6=Train
7=Ship/boat
8=Motorcycle
9=Bicycle
10=Motorcoach/bus
l 1=Other

Ratio

1=Ovemight trip

2=Day trip

Ratio

137

Modiﬁcation

1=Summer
0=All other

1=Visiting friends or
relatives
0=All other

1=Car/truck with/without
camping equipment
0=All other

1=Ovemight trip
0=Daytrip

1=Out-of-state destination
0=In-state destination

 

 

Variable Type Deﬁnition (mind Coding Modiﬁcation
Lodging in friends or Main type of 1=Friends or relative's l=Lodging in friends or
relative's home lodging used home relative's home

Activity participation

Travel expenditures

Planning horizon

Search for travel
information online

Participation in
selected activities

Dollar spending per
person per trip

Number of days
planning began in
advance

Use of the lntemet

to obtain travel
information

2=Hotel, motel, or
lodge

3=Bed & Breakfast
4=Rented cabin,
cottage, or
condominium
5=Owned cabin,
cottage, or
condominium
6=County, state, or
federal campground
7=Commercial
campground
8=Boat/ship
9=Other

1=Yes
2=No

Ratio

Ratio

1 =Yes
2=No

0=All other

Number of activates pursued
among 13 selected activities.

1=Search for travel
information online,
0=Otherwise

 

138

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139

 

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