Description
This study aims to ascertain the effect of socio-demographic constraints on dimension of
travel choice. This study also seeks to derive personal ecological explanations for variation in travel
preference, travel intention and travel choice behavior of a wide range of destinations.
International Journal of Culture, Tourism and Hospitality Research
Socio-demographic constraints to travel behavior
Uraiporn Kattiyapornpong Kenneth E. Miller
Article information:
To cite this document:
Uraiporn Kattiyapornpong Kenneth E. Miller, (2009),"Socio-demographic constraints to travel behavior", International J ournal of Culture,
Tourism and Hospitality Research, Vol. 3 Iss 1 pp. 81 - 94
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Socio-demographic constraints to travel
behavior
Uraiporn Kattiyapornpong and Kenneth E. Miller
Abstract
Purpose – This study aims to ascertain the effect of socio-demographic constraints on dimension of
travel choice. This study also seeks to derive personal ecological explanations for variation in travel
preference, travel intention and travel choice behavior of a wide range of destinations.
Design/methodology/approach – A large representative sample of 49,105 Australian respondents is
utilized. Binary logistic regression is used to determine the impact of constraint variables.
Findings – Age, income and life stage have signi?cant differential and interactive effects on travel
behavior. Socio-demographic variables act in different ways to constrain/free different types of travel
behavior. However there are signi?cant levels of travel by even the most constrained groups as well as
signi?cant amounts of non-travel by the least constrained sectors of our society. These impacts are
country speci?c.
Research limitations/implications – The travel motivations of constraint groups need to be
considered to order better understand travel behavior. Investigation of psychological and ecological
facilitators and constraints to travel is needed.
Practical implications – This information is most useful for market segmentation and the development
of constraint group destination marketing plans. Managers can use utilize such results to minimize the
barriers to travel by particular groups.
Originality/value – This paper utilizes a large database to provide insights into the personal ecological
constraints to travel.
Keywords Travel, Behavior, Self esteem, Australia
Paper type Research paper
C
onsumer behavior and travel and tourism marketing researchers devote
considerable attention to understanding the nature of travel choice. For example,
the work of Woodside et al. (2007) extends and applies ecological systems theory
using a narrative case study method to examine consumer leisure and travel behavior.
Samdahl and Jekubovich (1997) view constraints as a subset of reasons for not engaging in
a particular behavior. Several researchers (for example, Hudson, 2000; Samdahl and
Jekubovich, 1997; Tian et al., 1996; Woodside and Lysonski, 1989) study in?uences of
constraints on activities participation. Woodside et al. (2007) con?rm the usefulness of the
constraints interaction proposition for understanding and describing the factors resulting in
participation, as well as nonparticipation, behaviors. Many researchers (for example, Hsieh
et al., 1992; Taylor et al., 1993; Teaff and Turpin, 1996) ?nd that demographic variables are
related to aspects of travel choice. Woodside and Pitts (1976) suggest that demographic
variables may act as qualifying variables or constraining variables rather than determining
variables of travel behavior.
Empirical research on the role demographic and socioeconomic variables as travel
constraints is limited. This study aims to ascertain the effect of socio-demographic
DOI 10.1108/17506180910940360 VOL. 3 NO. 1 2009, pp. 81-94, Q Emerald Group Publishing Limited, ISSN 1750-6182
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PAGE 81
Uraiporn Kattiyapornpong
is based at Deakin
University, Burwood,
Australia. Kenneth E. Miller
is based at the University of
Technology, Sydney,
Australia.
Received January 2008
Revised February 2008
Accepted May 2008
The authors thank Roy Morgan
Research Centre for supplying
the data for this study. The
contribution of Arch Woodside,
Boston College is gratefully
acknowledged.
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constraints on dimensions of travel choice. A multidimensional measure of travel behavior is
used which combines destination choice sets and duration of travel. The effects of speci?c
nonlinear combinations of demographic variables, selected according to the leisure
constraint model on travel preference and choice are investigated. This study also seeks to
derive personal ecological explanations for variation in travel intention and travel choice
behavior of a wide range of destinations.
Background literature
A particularly comprehensive framework of a purchase consumption system applied to
leisure travel behavior was developed by Woodside et al. (2007). This framework illustrates
how demographic variables, socioeconomic variables and family effect travel intentions and
travel decisions. Travel preferences are generally less constrained by income and family
considerations and represent the places where persons would like to go. Actual travel
behavior can be more constrained by macrosystem variables such as age, income and life
stage. This research posits that age, income and life stage will have less of an effect on travel
preferences and a greater effect on travel choice (Samdahl and Jekubovich, 1997).
Ecological system theory
Ecological systems theory can be used to explain travel choice behavior by providing
valuable insights for predicting travel choice and behavior through the individuals’
environment. Bronfenbrenner (1986) proposes that the microsystem and the macrosystem
both in?uence behavior. Microsystem factors include the personal in?uences that effect an
individual’s decisions and choices. Prior activities in which an individual has participated in
are part of an individual’s microsystem because maintaining participation in this activity is
relatively easy (Woodside et al., 2004). The microsystemincludes activities that a person has
experienced as well as past and present roles of the individual. The macrosystem is the
larger context in which the individual functions and includes belief systems such as societal
conceptions of ethnicity, socioeconomic status, and gender, and other structures of society
and its institutions, including social class, gender, culture, money, and ethnicity (Floyd et al.,
1994). Many authors (for example, Brown and Boston, 1994; Hsieh et al., 1992; Lang et al.,
1997; Taylor et al., 1993; Teaff and Turpin, 1996) ?nd that demographic variables are related
to aspects of travel choice.
The leisure constraint model
The leisure constraint model is one of a number of theories that can be extended and related
to travel choice behavior. Crawford and Godbey (1987) propose a model of the relationship
of leisure barriers, preference and participation of family leisure. Figures 1, 2, and 3 illustrate
three barriers that affect the relationship between leisure preferences and participation:
intrapersonal barriers (Figure 1), interpersonal barriers (Figure 2) and structural barriers
(Figure 3).
Intrapersonal barriers involve individual psychological states and attributes such as stress,
depression anxiety, reference group attitudes, which interact with leisure preferences rather
than intervening between preferences and participation (Crawford and Godbey, 1987).
These barriers are somewhat unstable and possibly temporal.
Interpersonal barriers are the result of the relationship between individuals’ characteristics
(Crawford and Godbey, 1987). These are either the product of the intrapersonal barriers
which accompany spouses into the marital relationship, thus affecting joint preference for
Figure 1 Crawford and Godbey (1987)’s intrapersonal constraints
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speci?c leisure activities, or those barriers which arise as the result of spousal interaction.
Barriers of this sort may interact with both preference for and participation in leisure
activities.
Structural barriers represent constraints are conceptualized as intervening factors between
leisure preference and participation (Crawford and Godbey, 1987). Examples of structural
barriers are family life-cycle stage, family ?nancial resources, season, time, and work
commitments.
Crawford et al. (1991) extend and develop a classic model of leisure constraints (Figure 4)
and illustrate the important of understanding of how constraints effect choices among
people who are already participating. Leisure preferences are developed when
intrapersonal constraints are absent or their effects have been mitigated. The individual
may encounter constraints at the interpersonal level in the next step depending on the type
of activity. It occurs only when this type of constraint has been overcome that structural
constraints begin to be encountered (Crawford et al., 1991). Participation will result in the
absence of, or negotiation through, structural constraints. If structural constraints are
suf?ciently strong, however, the outcome will be nonparticipation.
An important contribution is made by Jackson (1997) and Samdahl and Jekubovich (1997)
who develop and discuss the roles of constraints and facilitators in choice of leisure activity.
Figure 2 Crawford and Godbey (1987)’s interpersonal constraints
Figure 3 Crawford and Godbey (1987)’s structural constraints
Figure 4 Crawford et al. (1991)’s hierarchical model of leisure constraints
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These authors argue that leisure constraints help to understand the in?uences that shape
people’s everyday leisure behavior and the differences in leisure choices for different
segments of the population (Samdahl and Jekubovich, 1997). While structural constraints
effect the type of leisure activity that people undertake, they do not prevent people from
engaging in leisure altogether. Applying these ideas to travel implies that degrees of travel
behavior should be studied, for example, travel distance and duration of holiday.
Raymore (2002) de?nes facilitators to leisure as ‘‘factors that are assumed by researchers
and perceived or experienced by individuals to enable or promote the formation of leisure
preferences and to encourage or enhance participation’’ (Raymore, 2002, 39). Clearly,
constraints and facilitators have been de?ned in almost identical ways. Constraints are in
fact negative versions of facilitator variables. Constraints and facilitators act together to
produce participation or nonparticipation, and considering both must is necessary when
discussing participation or nonparticipation from an ecological perspective (individuals
interact with the contexts in which they live their lives). He develops a multi-layer ecological
approach to understanding in?uences on leisure participation. Intrapersonal facilitators are
those individual characteristics, traits and beliefs that enable and encourage the formation
of leisure preferences (Raymore, 2002). Interpersonal facilitators are those individuals or
groups that encourage participation in leisure. Finally, structural facilitators those social and
physical institutions, organizations, or belief systems of a society that operate externally to
the individual to enable or promote the formation of leisure preferences and encourage or
enhance participation in leisure.
Woodside et al. (2007) con?rm the usefulness of the constraints (and facilitators) interaction
proposition in understanding and describing the factors which determine participation, as
well as nonparticipation behaviors. Travel constraints researchers (Hudson and Gilbert,
2000; Norman, 1995; Plog, 1974; Stemerding et al., 1996) focus on non-participants. Several
researchers (Aas, 1995; Kay and Jackson, 1991; Shaw et al., 1991; Wright and Goodale,
1991) ?nd differences among participants exhibiting different levels of participation
frequency and interest but ?nd no relationship between the reporting of some constraints
and actual leisure participation.
Travel constraints theory
A number of studies investigate constraints to travel behavior. It can be argued that travel
constraints are quite different from general leisure behavior in ways such as cost, duration
and commitment. It is likely that constraints and facilitators operate differently in impacting
travel behavior.
Many factors in?uence and constrain leisure travel. Age is a most important travel constraint.
For example, Romsa and Blenman (1989) study and compare the vacation patterns of the
elderly Germans in order to ascertain the in?uence of age and environmental factors on
tourist participation including modes of travel, destinations, length of vacation,
accommodations, popularity of activities, and vacation memories. They conclude that
socioeconomic, physical, psychological, and physiological (age related) constraints play an
important role in the underlying processes related to the behavior of the elderly vacationer.
Motivations for taking holidays vary by age group. Therefore taking vacations as leisure or
recreational experience declines with age. The more delicate physical condition of seniors
constrains the choice of vacation destination and holiday activities. Intergenerational effects
are likely to impact the travel of these older persons. Similarly, in the study of the relationship
between travel and the elderly, Teaff and Turpin (1996) ?nd that older Americans travel more
frequently and longer distances, stay away longer, and rely more on travel agents than other
segments of the population. Older people place a higher priority on visiting friends and
relatives. Some evidence shows that longer vacations are taken after the age of retirement.
Retirees are signi?cantly more likely to be constrained by the perception of age, disability,
health conditions, and physical energy. Being too busy to travel constrains the pre-retirees,
while physical in?rmity and less of an adventuresome spirit constrains retirees.
Income also signi?cantly in?uences travel choice behavior. Nicolau and Ma´ s (2005) study
tourist choice process using tourist expenditure. They ?nd that income, household size,
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education, size of the city of origin and opinion of going on holiday are determinants that
affect the decision to go on holiday. Personal restriction (income and household size)
signi?cantly relates to the decision to go on holiday, while socio-demographic
characteristics (age) is not signi?cant.
Children in?uence family travel decision. For example, Nickerson and Jurowski (2000) study
the in?uences of children on vacation travel patterns. They provide a perspective on
planning and development with a view to increase child satisfaction at the destination. The
family life cycle is also a signi?cant constraint to travel choice behavior. In a study of the
family life cycle (FLC) of German travelers, Oppermann (1995) concludes that FLC effects
travel patterns considerably. Many aspects of the tourists’ travel patterns relate to their
stages of the family life cycle.
In summary, age, income and life cycle are likely to be signi?cant constraints to leisure and
recreation activity. Prior empirical research is limited on the role demographic and
socioeconomic variables as travel constraints. This study aims to understand the role of
socio-demographic constraints on shaping travel behavior and investigate the amount of
travel by severely constrained groups. The effect of critical constraint interactions is
examined. This study also seeks to derive personal ecological explanations for variation in
travel intention and travel choice behavior.
Method
This research utilizes data generated from the Roy Morgan Research Centre in Australia
(RMRC). RMRC collected these data in 2003 and 2004 from a face-to-face survey and a
self-completion questionnaire survey. A large representative sample of 49,105 Australian
respondents was interviewed. Data were collected on a wide range of variables including
media habits, demographics, travel motivations, AIOs, consumer travel attitudes and
information of travel preferences, travel intentions over the next 12 months and travel
behavior over the last 12 months.
RMRC selects respondents using a strati?ed random probability sample to ensure correct
representation of all Australian states, major metropolitan and country areas. The
interviewing method ensures complete coverage spread evenly across all electorates,
with interviewers visiting different randomly selected clusters of dwellings.
RMRC conducts face-to-face interviews in the city and country areas of all six states and the
two territories, namely, Sydney, NSW country, including the ACT, Melbourne, Victoria
country, Brisbane, Queensland country, Adelaide, South Australia country and the Northern
Territory, Perth, Western Australia country, and Tasmania. Approximately 500 people were
interviewed each weekend for 12 weekends per quarter during 2003 and 2004.
RMRC designs the sample to be representative of the Australian population 14 years and
over. Only one person per household was interviewed. Re-contacting was used as a quality
control measure after each round of interviewing for a proportion of respondents. Collecting
the sample continuously over a two-year period means that samples of lower incidence
populations can be accumulated week by week to the desired size, both accurately and
economically. The RMRC used a consistent methodology over the two-year data collection
period.
After the face-to-face interview, respondents were asked to complete a self-completion
questionnaire administered survey including activities and interests, attitudes and opinions,
internet usage and other product data. Data from the establishment survey and product poll
were merged for each respondent to form a large, single-source database.
Findings
Combinations of age, income and life stage are utilized to develop 45 constraint groups. The
dependent variables of travel destination planned for the next 12 months and past travel
destination in the last 12 months are compared across the 45 constraint groups. Travel
dependent variables are measured according to the categories of intrastate travel, interstate
travel, close vs. distant proximity international travel (New Zealand, Asia, Americas and
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Europe) and duration of stay (short and long trips). Variables measuring the difference
between travel plans and past travel behavior are also operationalized for both short and
long trips duration. Table I shows the means of travel behavior for intrastate, interstate and
international long trips across the 45 constraint groups. Travel behavior is signi?cantly
different across combinations of age, income and life stage.
The relative main and interactive effects of the independent variables on the dependent
variables are con?rmed using binary logistic regression. The dependent variables used in
the analyses are destination planned intrastate short trips, destination planned interstate
short trips, past places intrastate short trips, past places interstate short trips, last places
intrastate short trips, last places interstate short trips, destination planned intrastate long
trips, destination planned interstate long trips, destination planned New Zealand long trips,
destination planned Asia long trips, destination planned America and Europe long trips, past
Table I Respondent travel choice set behavior for long trip
Past places
Intra State
Past places
Inter State
Past places
EU and USA
Past places
Asia
Past places
New Zealand
Constraint group Mean Mean Mean Mean Mean
Group 1 Low inc, single, 20 to 24 yrs 0.29 0.24 0.03 0.05 0.01
Group 2 Low inc, couple, 20 to 24 yrs 0.37 0.21 0.05 0.04 0.02
Group 3 Low inc, family, 20 to 24 yrs 0.20 0.12 0.00 0.00 0.02
Group 4 Medium inc, single, 20 to 24 yrs 0.34 0.34 0.04 0.04 0.02
Group 5 Medium inc, couple, 20 to 24 yrs 0.42 0.36 0.05 0.03 0.02
Group 6 Medium inc, family, 20 to 24 yrs 0.32 0.21 0.01 0.01 0.01
Group 7 High inc, single, 20 to 24 yrs 0.32 0.34 0.13 0.07 0.04
Group 8 High inc, couple, 20 to 24 yrs 0.41 0.42 0.07 0.06 0.01
Group 9 High inc, family, 20 to 24 yrs 0.22 0.22 0.00 0.04 0.09
Group 10 Low inc, single, 25 to 34 yrs 0.25 0.22 0.02 0.01 0.01
Group 11 Low inc, couple, 25 to 34 yrs 0.29 0.30 0.11 0.05 0.04
Group 12 Low inc, family, 25 to 34 yrs 0.29 0.19 0.01 0.02 0.01
Group 13 Medium inc, single, 25 to 34 yrs 0.30 0.35 0.07 0.07 0.03
Group 14 Medium inc, couple, 25 to 34 yrs 0.34 0.37 0.05 0.04 0.03
Group 15 Medium inc, family, 25 to 34 yrs 0.36 0.27 0.02 0.01 0.02
Group 16 High inc, single, 25 to 34 yrs 0.35 0.48 0.12 0.12 0.06
Group 17 High inc, couple, 25 to 34 yrs 0.43 0.51 0.11 0.09 0.08
Group 18 High inc, family, 25 to 34 yrs 0.45 0.38 0.03 0.02 0.04
Group 19 Low inc, single, 35 to 44 yrs 0.24 0.17 0.01 0.01 0.01
Group 20 Low inc, couple 35 to 44 yrs 0.25 0.22 0.03 0.04 0.02
Group 21 Low inc, family 35 to 44 yrs 0.27 0.18 0.01 0.02 0.01
Group 22 Medium inc, single 35 to 44 yrs 0.31 0.33 0.05 0.03 0.02
Group 23 Medium inc, couple 35 to 44 yrs 0.33 0.29 0.04 0.03 0.02
Group 24 Medium inc, family 35 to 44 yrs 0.43 0.27 0.02 0.02 0.01
Group 25 High inc, single 35 to 44 yrs 0.32 0.39 0.09 0.12 0.05
Group 26 High inc, couple 35 to 44 yrs 0.36 0.43 0.09 0.08 0.04
Group 27 High inc, family 35 to 44 yrs 0.48 0.45 0.06 0.04 0.03
Group 28 Low inc, single 45 to 54 yrs 0.19 0.19 0.02 0.01 0.01
Group 29 Low inc, couple 45 to 54 yrs 0.26 0.25 0.03 0.02 0.01
Group 30 Low inc, family 45 to 54 yrs 0.26 0.19 0.01 0.02 0.00
Group 31 Medium inc, single 45 to 54 yrs 0.31 0.35 0.07 0.05 0.02
Group 32 Medium inc, couple 45 to 54 yrs 0.37 0.34 0.04 0.04 0.03
Group 33 Medium inc, family 45 to 54 yrs 0.38 0.30 0.02 0.03 0.02
Group 34 High inc, single 45 to 54 yrs 0.32 0.40 0.10 0.04 0.04
Group 35 High inc, couple 45 to 54 yrs 0.42 0.47 0.08 0.07 0.05
Group 36 High inc, family 45 to 54 yrs 0.50 0.41 0.07 0.06 0.03
Group 37 Low inc, single 55 yrs and over 0.25 0.26 0.03 0.01 0.02
Group 38 Low inc, couple 55 yrs and over 0.34 0.32 0.03 0.02 0.01
Group 39 Low inc, family 55 yrs and over 0.21 0.19 0.02 0.04 0.01
Group 40 Medium inc, single 55 yrs and over 0.28 0.36 0.08 0.05 0.03
Group 41 Medium inc, couple 55 yrs and over 0.41 0.43 0.07 0.04 0.03
Group 42 Medium inc, family 55 yrs and over 0.27 0.30 0.06 0.03 0.03
Group 43 High inc, single 55 yrs and over 0.33 0.41 0.16 0.11 0.04
Group 44 High inc, couple 55 yrs and over 0.40 0.51 0.13 0.08 0.06
Group 45 High inc, family 55 yrs and over 0.35 0.41 0.07 0.05 0.04
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places intrastate long trips, past places interstate long trips, past places New Zealand long
trips, past places Asia long trips, past places America and Europe long trips, last places
intrastate long trips, last places interstate long trips, last places New Zealand long trips, last
places Asia long trips and last places America and Europe long trips.
Binary logistic regression is used to analyze the data as the dependent variables are
dichotomous. The deviation measure is used to calculate contrasts where each category of
the predictor variable except the reference category is compared to the overall effect.
Table II shows the means of the dependent variable (planned intrastate short trip) on all
independent variables.
Table III shows the results of the binary logistic regression using the dependent variable of
destination planned intrastate short trip plus the categorical independent variables of age,
income and life stage plus all interactions. Firstly, age signi?cantly contributes to the
discrimination. The results reveal that the probability to plan intrastate travel of the younger
respondents was the highest ( p , 0.05) while older respondents are less likely to travel.
Secondly, household income constitutes to the discrimination in consumer planning of a
short intrastate trip within the next 12 months. A closer inspection of the results reveals that
the highest income group is more likely to plan intrastate travel than lower income
consumers ( p , 0.05). Thirdly, life stage also signi?cantly contributes to the discrimination.
The results reveal that the probability to plan intrastate travel of the single life-stage
consumers is the lowest (beta ¼ 20:245, p , 0.05). Families are more likely to plan
intrastate travel.
Importantly, two interaction effects are signi?cant. Figure 5 illustrates the interaction effect of
income and life stage of consumers is signi?cant. For example, the interaction effect
between the low income and single group is 0.087 ( p , 0.05). Figure 6 illustrates the
interaction effect of income and age of consumers is also signi?cant. For example, the
interaction effect between the middle household income group and the 35 to 44 age group is
0.069 ( p , 0.05). The interaction between age and life stage is not signi?cant (Figure 7).
This analysis is repeated for all dependent variables, that is, interstate travel, close vs.
distant proximity international travel destinations (NewZealand, Asia, Americas and Europe)
and duration of stay (short and long trips).
Table IV provides a summary of the levels of signi?cance from binary logistic regression
using the dependent variables of travel planning and travel choice for Asian and NZ
destinations. For Asia, consistent results are found for income and life stage. Age is less of a
discriminator and therefore less useful as a segmentation variable. Travel to New Zealand is
Table II Mean value of destination planned intrastate short trip within the next 12 months
by age, income, and life stage
Demographic groups Destination planned intrastate short trip
Age groups
20 to 24 years old 0.54
25 to 34 years old 0.59
35 to 44 years old 0.57
45 to 54 years old 0.52
55 years old and over 0.36
Household income groups
Low income (under 30K) 0.35
Medium income (30K to 79.9K) 0.53
High income (80K or more) 0.59
Life stage groups
Single 0.41
Couple 0.46
Family 0.58
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Table III Binary logistic regression results for destination planned intrastate short trips
B SE Wald df Sig. Exp(B)
Age group 20 to 24 years old 0.31 0.06 31.09 1 0.00 1.36
Age group 25 to 34 years old 0.27 0.03 80.91 1 0.00 1.31
Age group 35 to 44 years old 0.05 0.03 3.12 1 0.08 1.05
Age group 45 to 54 years old 20.11 0.03 17.64 1 0.00 0.90
Age group 55 years old and over 20.52 339.08 4 0.00
HH inc. low income 20.32 0.02 191.36 1 0.00 0.73
HH inc. medium income 0.08 0.02 17.12 1 0.00 1.09
HH inc. high income 0.24 192.68 2 0.00
Life stage single 20.25 0.02 101.52 1 0.00 0.78
Life stage couple 0.11 0.03 20.09 1 0.00 1.12
Life stage family 0.13 102.10 2 0.00
HH income * Life stage 15.94 4 0.00
HH inc. low income by single 0.09 0.03 10.03 1 0.00 1.09
HH inc. low income by couple 20.01 0.03 0.06 1 0.81 0.99
HH inc. medium income by single 0.05 0.03 3.73 1 0.05 1.05
HH inc. medium income by couple 20.05 0.02 4.24 1 0.04 0.95
Age group * HH income 18.89 8 0.02
Age 20 to 24 yrs by low income 0.11 0.07 2.48 1 0.12 1.11
Age 20 to 24 yrs by medium income 20.12 0.06 3.86 1 0.05 0.89
Age 25 to 34 yrs by low income 0.04 0.05 0.65 1 0.42 1.04
Age 25 to 34 yrs by medium income 20.01 0.04 0.15 1 0.70 0.99
Age 35 to 44 yrs by low income 0.00 0.04 0.00 1 0.95 1.00
Age 35 to 44 yrs by medium income 0.07 0.03 4.41 1 0.04 1.07
Age 45 to 54 yrs by low income 20.02 0.04 0.37 1 0.54 0.98
Age 45 to 54 yrs by medium income 0.01 0.03 0.07 1 0.79 1.01
Age group * Life stage 10.37 8 0.24
Age 20 to 24 yrs by single 20.10 0.06 2.63 1 0.11 0.91
Age 20 to 24 yrs by couple 0.14 0.07 3.72 1 0.05 1.15
Age 25 to 34 yrs by single 0.01 0.04 0.08 1 0.77 1.01
Age 25 to 34 yrs by couple 20.05 0.04 1.06 1 0.30 0.96
Age 35 to 44 yrs by single 0.03 0.04 0.55 1 0.46 1.03
Age 35 to 44 yrs by couple 20.08 0.04 3.25 1 0.07 0.93
Age 45 to 54 yrs by single 0.05 0.04 1.78 1 0.18 1.05
Age 45 to 54 yrs by couple 20.06 0.03 3.02 1 0.08 0.94
Constant 0.08 0.02 20.02 1 0.00 1.09
Figure 5 Interactions between age and life stage with planned destination intrastate short
trips
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Figure 6 Interactions between income and life stage with planned destination intrastate
short trip
Figure 7 Interactions between income and age with destination planned intrastate short
trip
Table IV Summary of the levels of signi?cance in the binary logistic regression results for long trip dependent variable
Long trips
Constraint variables
Planned
places Asia
Planned places
NZ
Past places
Asia
Past places
NZ
Last stayed
places Asia
Last stayed
places NZ
Age 20 to 24 years old 0.09 0.01 0.32 0.45 0.44 0.80
Age 25 to 34 years old 0.91 0.06 0.93 0.06 0.89 0.16
Age 35 to 44 years old 0.81 0.41 0.65 0.12 0.99 0.10
Age 45 to 54 years old 0.99 0.63 0.29 0.20 0.69 0.22
Age 55 years old and over 0.16 0.00 0.31 0.01 0.66 0.04
Low income 0.00 0.00 0.00 0.00 0.00 0.00
Medium income 0.13 0.97 0.35 0.75 0.87 0.84
High income 0.00 0.00 0.00 0.00 0.00 0.00
Single 0.00 0.48 0.00 0.27 0.02 0.81
Couple 0.35 0.07 0.01 0.14 0.00 0.26
Family 0.00 0.10 0.00 0.11 0.00 0.44
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not demographically determined. This destination appeals equally to all demographic
groups.
The constrained respondent groups are identi?ed and compared to the less constrained
respondents. For example, low income families are particularly constrained for short stay
interstate travel. However, 10 percent of this group did travel interstate within the last
12 month period compared to nearly 40 percent of high income persons (both singles and
couples) who are aged between 25 and 34 years. Findings are also illustrated for longer stay
domestic and international travel. Explanation should be sought for the case where
respondents preferred a travel destination but did not plan to travel to this destination.
Tables V, VI and VII show summaries of the level of signi?cance in the binary logistic
regression results for the long trip dependent variables and the two-way interactions of
income and life stage, age and income, and age and life stage, respectively.
As expected, signi?cant interactions are more prevalent for the Asian destinations than for
New Zealand. The Asian destinations appeal to particular constraint groups more than
others. This might be due to distance, perceived security and language differences. New
Zealand has more consistent appeal regardless of the constraint group, however signi?cant
age and life stage interactions exist.
Research limitations/implications
This research considers the effects of important demographic and socioeconomic travel
constraints but does not consider psychological constraints. These secondary data were
collected between 2003 and 2004 and were designed by the Roy Morgan Research Centre.
However, this research does not measure a full range of constraint variables. The travel
motivations of constraint groups need to be considered in order to better understand travel
Table VI Summary of the levels of signi?cance in the binary logistic regression results of two-way interaction of age and
income for long trip dependent variable
Long trips
Interactions
Planned
places Asia
Planned
places NZ
Past places
Asia
Past places
NZ
Last stayed
places Asia
Last stayed
places NZ
Age group * HH income 0.00 0.80 0.01 0.82 0.01 0.97
Age 20 to 24 years by low income 0.02 0.36 0.00 0.21 0.00 0.57
Age 20 to 24 years by medium income 0.04 0.24 0.11 0.40 0.51 0.35
Age 25 to 34 years by low income 0.96 0.87 0.30 0.78 0.51 0.89
Age 25 to 34 years by medium income 0.31 0.71 0.27 0.95 0.30 0.68
Age 35 to 44 years by low income 0.25 0.92 0.77 0.88 0.55 0.72
Age 35 to 44 years by medium income 0.91 0.95 0.25 0.74 0.06 0.74
Age 45 to 54 years by low income 0.21 0.67 0.10 0.13 0.04 0.64
Age 45 to 54 years by medium income 0.14 0.73 0.35 0.12 0.24 0.25
Table V Summary of the levels of signi?cance in the binary logistic regression results of two-way interaction of income and
life stage for long trip dependent variable
Long trips
Interactions
Planned
places Asia
Planned places
NZ
Past places
Asia
Past places
NZ
Last stayed
places Asia
Last stayed
places NZ
HH Income * Life stage 0.00 0.90 0.00 0.94 0.00 0.81
Low income by single 0.00 0.78 0.00 0.40 0.00 0.39
Low income by couple 0.91 0.34 0.97 0.98 0.51 0.90
Medium income by single 0.00 0.73 0.01 0.68 0.01 0.81
Medium income by couple 0.33 0.73 0.60 0.91 0.55 0.96
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behavior. Investigation of psychological and ecological facilitators and constraints to travel
is needed.
Practical implications
Destination and tourism managers can utilize such results to minimize the barriers to travel
by particular groups. For example, two-income younger households with no children are
time constrained. Attractive destination packages can be designed around short get-away
themes. Speci?c tour packages and tour incentives can be designed around constraint
groups de?ned by age, income and life stage.
The similarities and differences between less and more constrained travellers, and less and
more constrained non-travellers can be a critical base for market segmentation of the
Australian market to both domestic and overseas destinations.
The results of this paper provide a pro?le of Australian travellers by age, income and life
stage. These variables can be effectively used in designing market strategies for Australian
travel markets. The ?ndings are important for those interested in travel markets. For
example, a nearby destination such as New Zealand should not necessarily utilize
demographics as a basis for segmentation. Travel marketers might focus on developing
attractive vacation packages addressing travellers’ activities, interests and opinions (AIOs),
lifestyle, and motivation for travel. Asian destinations should carefully review critical
combinations of constraint variables to identify high potential groups. Minimization of the
effects of constraints on low potential groups should also be considered. Marketers of
tourism in general may wish to consider the marketing implications of this paper. A different
marketing message should be communicated using different travel constraints and
facilitators (macrosystems and microsystems) identi?ed among the 45 constraint groups.
Less constrained groups travel more than the more constrained groups; however the paper
shows that particular segments within the constraint groups also undertake signi?cant travel.
Conclusions
This paper ?nds that age, income and life stage have signi?cant differential and interactive
effects on travel behavior. The results show that socio-demographic variables act in different
ways to constrain/free different types of travel behavior. Even the most constrained groups
undertake signi?cant travel. Many people in least constrained sectors of our society do not
travel. These phenomena need to be understood and current research is addressing these
issues.
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Corresponding author
Kenneth E. Miller can be contacted at: [email protected]
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doc_787300737.pdf
This study aims to ascertain the effect of socio-demographic constraints on dimension of
travel choice. This study also seeks to derive personal ecological explanations for variation in travel
preference, travel intention and travel choice behavior of a wide range of destinations.
International Journal of Culture, Tourism and Hospitality Research
Socio-demographic constraints to travel behavior
Uraiporn Kattiyapornpong Kenneth E. Miller
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To cite this document:
Uraiporn Kattiyapornpong Kenneth E. Miller, (2009),"Socio-demographic constraints to travel behavior", International J ournal of Culture,
Tourism and Hospitality Research, Vol. 3 Iss 1 pp. 81 - 94
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Socio-demographic constraints to travel
behavior
Uraiporn Kattiyapornpong and Kenneth E. Miller
Abstract
Purpose – This study aims to ascertain the effect of socio-demographic constraints on dimension of
travel choice. This study also seeks to derive personal ecological explanations for variation in travel
preference, travel intention and travel choice behavior of a wide range of destinations.
Design/methodology/approach – A large representative sample of 49,105 Australian respondents is
utilized. Binary logistic regression is used to determine the impact of constraint variables.
Findings – Age, income and life stage have signi?cant differential and interactive effects on travel
behavior. Socio-demographic variables act in different ways to constrain/free different types of travel
behavior. However there are signi?cant levels of travel by even the most constrained groups as well as
signi?cant amounts of non-travel by the least constrained sectors of our society. These impacts are
country speci?c.
Research limitations/implications – The travel motivations of constraint groups need to be
considered to order better understand travel behavior. Investigation of psychological and ecological
facilitators and constraints to travel is needed.
Practical implications – This information is most useful for market segmentation and the development
of constraint group destination marketing plans. Managers can use utilize such results to minimize the
barriers to travel by particular groups.
Originality/value – This paper utilizes a large database to provide insights into the personal ecological
constraints to travel.
Keywords Travel, Behavior, Self esteem, Australia
Paper type Research paper
C
onsumer behavior and travel and tourism marketing researchers devote
considerable attention to understanding the nature of travel choice. For example,
the work of Woodside et al. (2007) extends and applies ecological systems theory
using a narrative case study method to examine consumer leisure and travel behavior.
Samdahl and Jekubovich (1997) view constraints as a subset of reasons for not engaging in
a particular behavior. Several researchers (for example, Hudson, 2000; Samdahl and
Jekubovich, 1997; Tian et al., 1996; Woodside and Lysonski, 1989) study in?uences of
constraints on activities participation. Woodside et al. (2007) con?rm the usefulness of the
constraints interaction proposition for understanding and describing the factors resulting in
participation, as well as nonparticipation, behaviors. Many researchers (for example, Hsieh
et al., 1992; Taylor et al., 1993; Teaff and Turpin, 1996) ?nd that demographic variables are
related to aspects of travel choice. Woodside and Pitts (1976) suggest that demographic
variables may act as qualifying variables or constraining variables rather than determining
variables of travel behavior.
Empirical research on the role demographic and socioeconomic variables as travel
constraints is limited. This study aims to ascertain the effect of socio-demographic
DOI 10.1108/17506180910940360 VOL. 3 NO. 1 2009, pp. 81-94, Q Emerald Group Publishing Limited, ISSN 1750-6182
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PAGE 81
Uraiporn Kattiyapornpong
is based at Deakin
University, Burwood,
Australia. Kenneth E. Miller
is based at the University of
Technology, Sydney,
Australia.
Received January 2008
Revised February 2008
Accepted May 2008
The authors thank Roy Morgan
Research Centre for supplying
the data for this study. The
contribution of Arch Woodside,
Boston College is gratefully
acknowledged.
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constraints on dimensions of travel choice. A multidimensional measure of travel behavior is
used which combines destination choice sets and duration of travel. The effects of speci?c
nonlinear combinations of demographic variables, selected according to the leisure
constraint model on travel preference and choice are investigated. This study also seeks to
derive personal ecological explanations for variation in travel intention and travel choice
behavior of a wide range of destinations.
Background literature
A particularly comprehensive framework of a purchase consumption system applied to
leisure travel behavior was developed by Woodside et al. (2007). This framework illustrates
how demographic variables, socioeconomic variables and family effect travel intentions and
travel decisions. Travel preferences are generally less constrained by income and family
considerations and represent the places where persons would like to go. Actual travel
behavior can be more constrained by macrosystem variables such as age, income and life
stage. This research posits that age, income and life stage will have less of an effect on travel
preferences and a greater effect on travel choice (Samdahl and Jekubovich, 1997).
Ecological system theory
Ecological systems theory can be used to explain travel choice behavior by providing
valuable insights for predicting travel choice and behavior through the individuals’
environment. Bronfenbrenner (1986) proposes that the microsystem and the macrosystem
both in?uence behavior. Microsystem factors include the personal in?uences that effect an
individual’s decisions and choices. Prior activities in which an individual has participated in
are part of an individual’s microsystem because maintaining participation in this activity is
relatively easy (Woodside et al., 2004). The microsystemincludes activities that a person has
experienced as well as past and present roles of the individual. The macrosystem is the
larger context in which the individual functions and includes belief systems such as societal
conceptions of ethnicity, socioeconomic status, and gender, and other structures of society
and its institutions, including social class, gender, culture, money, and ethnicity (Floyd et al.,
1994). Many authors (for example, Brown and Boston, 1994; Hsieh et al., 1992; Lang et al.,
1997; Taylor et al., 1993; Teaff and Turpin, 1996) ?nd that demographic variables are related
to aspects of travel choice.
The leisure constraint model
The leisure constraint model is one of a number of theories that can be extended and related
to travel choice behavior. Crawford and Godbey (1987) propose a model of the relationship
of leisure barriers, preference and participation of family leisure. Figures 1, 2, and 3 illustrate
three barriers that affect the relationship between leisure preferences and participation:
intrapersonal barriers (Figure 1), interpersonal barriers (Figure 2) and structural barriers
(Figure 3).
Intrapersonal barriers involve individual psychological states and attributes such as stress,
depression anxiety, reference group attitudes, which interact with leisure preferences rather
than intervening between preferences and participation (Crawford and Godbey, 1987).
These barriers are somewhat unstable and possibly temporal.
Interpersonal barriers are the result of the relationship between individuals’ characteristics
(Crawford and Godbey, 1987). These are either the product of the intrapersonal barriers
which accompany spouses into the marital relationship, thus affecting joint preference for
Figure 1 Crawford and Godbey (1987)’s intrapersonal constraints
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speci?c leisure activities, or those barriers which arise as the result of spousal interaction.
Barriers of this sort may interact with both preference for and participation in leisure
activities.
Structural barriers represent constraints are conceptualized as intervening factors between
leisure preference and participation (Crawford and Godbey, 1987). Examples of structural
barriers are family life-cycle stage, family ?nancial resources, season, time, and work
commitments.
Crawford et al. (1991) extend and develop a classic model of leisure constraints (Figure 4)
and illustrate the important of understanding of how constraints effect choices among
people who are already participating. Leisure preferences are developed when
intrapersonal constraints are absent or their effects have been mitigated. The individual
may encounter constraints at the interpersonal level in the next step depending on the type
of activity. It occurs only when this type of constraint has been overcome that structural
constraints begin to be encountered (Crawford et al., 1991). Participation will result in the
absence of, or negotiation through, structural constraints. If structural constraints are
suf?ciently strong, however, the outcome will be nonparticipation.
An important contribution is made by Jackson (1997) and Samdahl and Jekubovich (1997)
who develop and discuss the roles of constraints and facilitators in choice of leisure activity.
Figure 2 Crawford and Godbey (1987)’s interpersonal constraints
Figure 3 Crawford and Godbey (1987)’s structural constraints
Figure 4 Crawford et al. (1991)’s hierarchical model of leisure constraints
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These authors argue that leisure constraints help to understand the in?uences that shape
people’s everyday leisure behavior and the differences in leisure choices for different
segments of the population (Samdahl and Jekubovich, 1997). While structural constraints
effect the type of leisure activity that people undertake, they do not prevent people from
engaging in leisure altogether. Applying these ideas to travel implies that degrees of travel
behavior should be studied, for example, travel distance and duration of holiday.
Raymore (2002) de?nes facilitators to leisure as ‘‘factors that are assumed by researchers
and perceived or experienced by individuals to enable or promote the formation of leisure
preferences and to encourage or enhance participation’’ (Raymore, 2002, 39). Clearly,
constraints and facilitators have been de?ned in almost identical ways. Constraints are in
fact negative versions of facilitator variables. Constraints and facilitators act together to
produce participation or nonparticipation, and considering both must is necessary when
discussing participation or nonparticipation from an ecological perspective (individuals
interact with the contexts in which they live their lives). He develops a multi-layer ecological
approach to understanding in?uences on leisure participation. Intrapersonal facilitators are
those individual characteristics, traits and beliefs that enable and encourage the formation
of leisure preferences (Raymore, 2002). Interpersonal facilitators are those individuals or
groups that encourage participation in leisure. Finally, structural facilitators those social and
physical institutions, organizations, or belief systems of a society that operate externally to
the individual to enable or promote the formation of leisure preferences and encourage or
enhance participation in leisure.
Woodside et al. (2007) con?rm the usefulness of the constraints (and facilitators) interaction
proposition in understanding and describing the factors which determine participation, as
well as nonparticipation behaviors. Travel constraints researchers (Hudson and Gilbert,
2000; Norman, 1995; Plog, 1974; Stemerding et al., 1996) focus on non-participants. Several
researchers (Aas, 1995; Kay and Jackson, 1991; Shaw et al., 1991; Wright and Goodale,
1991) ?nd differences among participants exhibiting different levels of participation
frequency and interest but ?nd no relationship between the reporting of some constraints
and actual leisure participation.
Travel constraints theory
A number of studies investigate constraints to travel behavior. It can be argued that travel
constraints are quite different from general leisure behavior in ways such as cost, duration
and commitment. It is likely that constraints and facilitators operate differently in impacting
travel behavior.
Many factors in?uence and constrain leisure travel. Age is a most important travel constraint.
For example, Romsa and Blenman (1989) study and compare the vacation patterns of the
elderly Germans in order to ascertain the in?uence of age and environmental factors on
tourist participation including modes of travel, destinations, length of vacation,
accommodations, popularity of activities, and vacation memories. They conclude that
socioeconomic, physical, psychological, and physiological (age related) constraints play an
important role in the underlying processes related to the behavior of the elderly vacationer.
Motivations for taking holidays vary by age group. Therefore taking vacations as leisure or
recreational experience declines with age. The more delicate physical condition of seniors
constrains the choice of vacation destination and holiday activities. Intergenerational effects
are likely to impact the travel of these older persons. Similarly, in the study of the relationship
between travel and the elderly, Teaff and Turpin (1996) ?nd that older Americans travel more
frequently and longer distances, stay away longer, and rely more on travel agents than other
segments of the population. Older people place a higher priority on visiting friends and
relatives. Some evidence shows that longer vacations are taken after the age of retirement.
Retirees are signi?cantly more likely to be constrained by the perception of age, disability,
health conditions, and physical energy. Being too busy to travel constrains the pre-retirees,
while physical in?rmity and less of an adventuresome spirit constrains retirees.
Income also signi?cantly in?uences travel choice behavior. Nicolau and Ma´ s (2005) study
tourist choice process using tourist expenditure. They ?nd that income, household size,
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education, size of the city of origin and opinion of going on holiday are determinants that
affect the decision to go on holiday. Personal restriction (income and household size)
signi?cantly relates to the decision to go on holiday, while socio-demographic
characteristics (age) is not signi?cant.
Children in?uence family travel decision. For example, Nickerson and Jurowski (2000) study
the in?uences of children on vacation travel patterns. They provide a perspective on
planning and development with a view to increase child satisfaction at the destination. The
family life cycle is also a signi?cant constraint to travel choice behavior. In a study of the
family life cycle (FLC) of German travelers, Oppermann (1995) concludes that FLC effects
travel patterns considerably. Many aspects of the tourists’ travel patterns relate to their
stages of the family life cycle.
In summary, age, income and life cycle are likely to be signi?cant constraints to leisure and
recreation activity. Prior empirical research is limited on the role demographic and
socioeconomic variables as travel constraints. This study aims to understand the role of
socio-demographic constraints on shaping travel behavior and investigate the amount of
travel by severely constrained groups. The effect of critical constraint interactions is
examined. This study also seeks to derive personal ecological explanations for variation in
travel intention and travel choice behavior.
Method
This research utilizes data generated from the Roy Morgan Research Centre in Australia
(RMRC). RMRC collected these data in 2003 and 2004 from a face-to-face survey and a
self-completion questionnaire survey. A large representative sample of 49,105 Australian
respondents was interviewed. Data were collected on a wide range of variables including
media habits, demographics, travel motivations, AIOs, consumer travel attitudes and
information of travel preferences, travel intentions over the next 12 months and travel
behavior over the last 12 months.
RMRC selects respondents using a strati?ed random probability sample to ensure correct
representation of all Australian states, major metropolitan and country areas. The
interviewing method ensures complete coverage spread evenly across all electorates,
with interviewers visiting different randomly selected clusters of dwellings.
RMRC conducts face-to-face interviews in the city and country areas of all six states and the
two territories, namely, Sydney, NSW country, including the ACT, Melbourne, Victoria
country, Brisbane, Queensland country, Adelaide, South Australia country and the Northern
Territory, Perth, Western Australia country, and Tasmania. Approximately 500 people were
interviewed each weekend for 12 weekends per quarter during 2003 and 2004.
RMRC designs the sample to be representative of the Australian population 14 years and
over. Only one person per household was interviewed. Re-contacting was used as a quality
control measure after each round of interviewing for a proportion of respondents. Collecting
the sample continuously over a two-year period means that samples of lower incidence
populations can be accumulated week by week to the desired size, both accurately and
economically. The RMRC used a consistent methodology over the two-year data collection
period.
After the face-to-face interview, respondents were asked to complete a self-completion
questionnaire administered survey including activities and interests, attitudes and opinions,
internet usage and other product data. Data from the establishment survey and product poll
were merged for each respondent to form a large, single-source database.
Findings
Combinations of age, income and life stage are utilized to develop 45 constraint groups. The
dependent variables of travel destination planned for the next 12 months and past travel
destination in the last 12 months are compared across the 45 constraint groups. Travel
dependent variables are measured according to the categories of intrastate travel, interstate
travel, close vs. distant proximity international travel (New Zealand, Asia, Americas and
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Europe) and duration of stay (short and long trips). Variables measuring the difference
between travel plans and past travel behavior are also operationalized for both short and
long trips duration. Table I shows the means of travel behavior for intrastate, interstate and
international long trips across the 45 constraint groups. Travel behavior is signi?cantly
different across combinations of age, income and life stage.
The relative main and interactive effects of the independent variables on the dependent
variables are con?rmed using binary logistic regression. The dependent variables used in
the analyses are destination planned intrastate short trips, destination planned interstate
short trips, past places intrastate short trips, past places interstate short trips, last places
intrastate short trips, last places interstate short trips, destination planned intrastate long
trips, destination planned interstate long trips, destination planned New Zealand long trips,
destination planned Asia long trips, destination planned America and Europe long trips, past
Table I Respondent travel choice set behavior for long trip
Past places
Intra State
Past places
Inter State
Past places
EU and USA
Past places
Asia
Past places
New Zealand
Constraint group Mean Mean Mean Mean Mean
Group 1 Low inc, single, 20 to 24 yrs 0.29 0.24 0.03 0.05 0.01
Group 2 Low inc, couple, 20 to 24 yrs 0.37 0.21 0.05 0.04 0.02
Group 3 Low inc, family, 20 to 24 yrs 0.20 0.12 0.00 0.00 0.02
Group 4 Medium inc, single, 20 to 24 yrs 0.34 0.34 0.04 0.04 0.02
Group 5 Medium inc, couple, 20 to 24 yrs 0.42 0.36 0.05 0.03 0.02
Group 6 Medium inc, family, 20 to 24 yrs 0.32 0.21 0.01 0.01 0.01
Group 7 High inc, single, 20 to 24 yrs 0.32 0.34 0.13 0.07 0.04
Group 8 High inc, couple, 20 to 24 yrs 0.41 0.42 0.07 0.06 0.01
Group 9 High inc, family, 20 to 24 yrs 0.22 0.22 0.00 0.04 0.09
Group 10 Low inc, single, 25 to 34 yrs 0.25 0.22 0.02 0.01 0.01
Group 11 Low inc, couple, 25 to 34 yrs 0.29 0.30 0.11 0.05 0.04
Group 12 Low inc, family, 25 to 34 yrs 0.29 0.19 0.01 0.02 0.01
Group 13 Medium inc, single, 25 to 34 yrs 0.30 0.35 0.07 0.07 0.03
Group 14 Medium inc, couple, 25 to 34 yrs 0.34 0.37 0.05 0.04 0.03
Group 15 Medium inc, family, 25 to 34 yrs 0.36 0.27 0.02 0.01 0.02
Group 16 High inc, single, 25 to 34 yrs 0.35 0.48 0.12 0.12 0.06
Group 17 High inc, couple, 25 to 34 yrs 0.43 0.51 0.11 0.09 0.08
Group 18 High inc, family, 25 to 34 yrs 0.45 0.38 0.03 0.02 0.04
Group 19 Low inc, single, 35 to 44 yrs 0.24 0.17 0.01 0.01 0.01
Group 20 Low inc, couple 35 to 44 yrs 0.25 0.22 0.03 0.04 0.02
Group 21 Low inc, family 35 to 44 yrs 0.27 0.18 0.01 0.02 0.01
Group 22 Medium inc, single 35 to 44 yrs 0.31 0.33 0.05 0.03 0.02
Group 23 Medium inc, couple 35 to 44 yrs 0.33 0.29 0.04 0.03 0.02
Group 24 Medium inc, family 35 to 44 yrs 0.43 0.27 0.02 0.02 0.01
Group 25 High inc, single 35 to 44 yrs 0.32 0.39 0.09 0.12 0.05
Group 26 High inc, couple 35 to 44 yrs 0.36 0.43 0.09 0.08 0.04
Group 27 High inc, family 35 to 44 yrs 0.48 0.45 0.06 0.04 0.03
Group 28 Low inc, single 45 to 54 yrs 0.19 0.19 0.02 0.01 0.01
Group 29 Low inc, couple 45 to 54 yrs 0.26 0.25 0.03 0.02 0.01
Group 30 Low inc, family 45 to 54 yrs 0.26 0.19 0.01 0.02 0.00
Group 31 Medium inc, single 45 to 54 yrs 0.31 0.35 0.07 0.05 0.02
Group 32 Medium inc, couple 45 to 54 yrs 0.37 0.34 0.04 0.04 0.03
Group 33 Medium inc, family 45 to 54 yrs 0.38 0.30 0.02 0.03 0.02
Group 34 High inc, single 45 to 54 yrs 0.32 0.40 0.10 0.04 0.04
Group 35 High inc, couple 45 to 54 yrs 0.42 0.47 0.08 0.07 0.05
Group 36 High inc, family 45 to 54 yrs 0.50 0.41 0.07 0.06 0.03
Group 37 Low inc, single 55 yrs and over 0.25 0.26 0.03 0.01 0.02
Group 38 Low inc, couple 55 yrs and over 0.34 0.32 0.03 0.02 0.01
Group 39 Low inc, family 55 yrs and over 0.21 0.19 0.02 0.04 0.01
Group 40 Medium inc, single 55 yrs and over 0.28 0.36 0.08 0.05 0.03
Group 41 Medium inc, couple 55 yrs and over 0.41 0.43 0.07 0.04 0.03
Group 42 Medium inc, family 55 yrs and over 0.27 0.30 0.06 0.03 0.03
Group 43 High inc, single 55 yrs and over 0.33 0.41 0.16 0.11 0.04
Group 44 High inc, couple 55 yrs and over 0.40 0.51 0.13 0.08 0.06
Group 45 High inc, family 55 yrs and over 0.35 0.41 0.07 0.05 0.04
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places intrastate long trips, past places interstate long trips, past places New Zealand long
trips, past places Asia long trips, past places America and Europe long trips, last places
intrastate long trips, last places interstate long trips, last places New Zealand long trips, last
places Asia long trips and last places America and Europe long trips.
Binary logistic regression is used to analyze the data as the dependent variables are
dichotomous. The deviation measure is used to calculate contrasts where each category of
the predictor variable except the reference category is compared to the overall effect.
Table II shows the means of the dependent variable (planned intrastate short trip) on all
independent variables.
Table III shows the results of the binary logistic regression using the dependent variable of
destination planned intrastate short trip plus the categorical independent variables of age,
income and life stage plus all interactions. Firstly, age signi?cantly contributes to the
discrimination. The results reveal that the probability to plan intrastate travel of the younger
respondents was the highest ( p , 0.05) while older respondents are less likely to travel.
Secondly, household income constitutes to the discrimination in consumer planning of a
short intrastate trip within the next 12 months. A closer inspection of the results reveals that
the highest income group is more likely to plan intrastate travel than lower income
consumers ( p , 0.05). Thirdly, life stage also signi?cantly contributes to the discrimination.
The results reveal that the probability to plan intrastate travel of the single life-stage
consumers is the lowest (beta ¼ 20:245, p , 0.05). Families are more likely to plan
intrastate travel.
Importantly, two interaction effects are signi?cant. Figure 5 illustrates the interaction effect of
income and life stage of consumers is signi?cant. For example, the interaction effect
between the low income and single group is 0.087 ( p , 0.05). Figure 6 illustrates the
interaction effect of income and age of consumers is also signi?cant. For example, the
interaction effect between the middle household income group and the 35 to 44 age group is
0.069 ( p , 0.05). The interaction between age and life stage is not signi?cant (Figure 7).
This analysis is repeated for all dependent variables, that is, interstate travel, close vs.
distant proximity international travel destinations (NewZealand, Asia, Americas and Europe)
and duration of stay (short and long trips).
Table IV provides a summary of the levels of signi?cance from binary logistic regression
using the dependent variables of travel planning and travel choice for Asian and NZ
destinations. For Asia, consistent results are found for income and life stage. Age is less of a
discriminator and therefore less useful as a segmentation variable. Travel to New Zealand is
Table II Mean value of destination planned intrastate short trip within the next 12 months
by age, income, and life stage
Demographic groups Destination planned intrastate short trip
Age groups
20 to 24 years old 0.54
25 to 34 years old 0.59
35 to 44 years old 0.57
45 to 54 years old 0.52
55 years old and over 0.36
Household income groups
Low income (under 30K) 0.35
Medium income (30K to 79.9K) 0.53
High income (80K or more) 0.59
Life stage groups
Single 0.41
Couple 0.46
Family 0.58
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Table III Binary logistic regression results for destination planned intrastate short trips
B SE Wald df Sig. Exp(B)
Age group 20 to 24 years old 0.31 0.06 31.09 1 0.00 1.36
Age group 25 to 34 years old 0.27 0.03 80.91 1 0.00 1.31
Age group 35 to 44 years old 0.05 0.03 3.12 1 0.08 1.05
Age group 45 to 54 years old 20.11 0.03 17.64 1 0.00 0.90
Age group 55 years old and over 20.52 339.08 4 0.00
HH inc. low income 20.32 0.02 191.36 1 0.00 0.73
HH inc. medium income 0.08 0.02 17.12 1 0.00 1.09
HH inc. high income 0.24 192.68 2 0.00
Life stage single 20.25 0.02 101.52 1 0.00 0.78
Life stage couple 0.11 0.03 20.09 1 0.00 1.12
Life stage family 0.13 102.10 2 0.00
HH income * Life stage 15.94 4 0.00
HH inc. low income by single 0.09 0.03 10.03 1 0.00 1.09
HH inc. low income by couple 20.01 0.03 0.06 1 0.81 0.99
HH inc. medium income by single 0.05 0.03 3.73 1 0.05 1.05
HH inc. medium income by couple 20.05 0.02 4.24 1 0.04 0.95
Age group * HH income 18.89 8 0.02
Age 20 to 24 yrs by low income 0.11 0.07 2.48 1 0.12 1.11
Age 20 to 24 yrs by medium income 20.12 0.06 3.86 1 0.05 0.89
Age 25 to 34 yrs by low income 0.04 0.05 0.65 1 0.42 1.04
Age 25 to 34 yrs by medium income 20.01 0.04 0.15 1 0.70 0.99
Age 35 to 44 yrs by low income 0.00 0.04 0.00 1 0.95 1.00
Age 35 to 44 yrs by medium income 0.07 0.03 4.41 1 0.04 1.07
Age 45 to 54 yrs by low income 20.02 0.04 0.37 1 0.54 0.98
Age 45 to 54 yrs by medium income 0.01 0.03 0.07 1 0.79 1.01
Age group * Life stage 10.37 8 0.24
Age 20 to 24 yrs by single 20.10 0.06 2.63 1 0.11 0.91
Age 20 to 24 yrs by couple 0.14 0.07 3.72 1 0.05 1.15
Age 25 to 34 yrs by single 0.01 0.04 0.08 1 0.77 1.01
Age 25 to 34 yrs by couple 20.05 0.04 1.06 1 0.30 0.96
Age 35 to 44 yrs by single 0.03 0.04 0.55 1 0.46 1.03
Age 35 to 44 yrs by couple 20.08 0.04 3.25 1 0.07 0.93
Age 45 to 54 yrs by single 0.05 0.04 1.78 1 0.18 1.05
Age 45 to 54 yrs by couple 20.06 0.03 3.02 1 0.08 0.94
Constant 0.08 0.02 20.02 1 0.00 1.09
Figure 5 Interactions between age and life stage with planned destination intrastate short
trips
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Figure 6 Interactions between income and life stage with planned destination intrastate
short trip
Figure 7 Interactions between income and age with destination planned intrastate short
trip
Table IV Summary of the levels of signi?cance in the binary logistic regression results for long trip dependent variable
Long trips
Constraint variables
Planned
places Asia
Planned places
NZ
Past places
Asia
Past places
NZ
Last stayed
places Asia
Last stayed
places NZ
Age 20 to 24 years old 0.09 0.01 0.32 0.45 0.44 0.80
Age 25 to 34 years old 0.91 0.06 0.93 0.06 0.89 0.16
Age 35 to 44 years old 0.81 0.41 0.65 0.12 0.99 0.10
Age 45 to 54 years old 0.99 0.63 0.29 0.20 0.69 0.22
Age 55 years old and over 0.16 0.00 0.31 0.01 0.66 0.04
Low income 0.00 0.00 0.00 0.00 0.00 0.00
Medium income 0.13 0.97 0.35 0.75 0.87 0.84
High income 0.00 0.00 0.00 0.00 0.00 0.00
Single 0.00 0.48 0.00 0.27 0.02 0.81
Couple 0.35 0.07 0.01 0.14 0.00 0.26
Family 0.00 0.10 0.00 0.11 0.00 0.44
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not demographically determined. This destination appeals equally to all demographic
groups.
The constrained respondent groups are identi?ed and compared to the less constrained
respondents. For example, low income families are particularly constrained for short stay
interstate travel. However, 10 percent of this group did travel interstate within the last
12 month period compared to nearly 40 percent of high income persons (both singles and
couples) who are aged between 25 and 34 years. Findings are also illustrated for longer stay
domestic and international travel. Explanation should be sought for the case where
respondents preferred a travel destination but did not plan to travel to this destination.
Tables V, VI and VII show summaries of the level of signi?cance in the binary logistic
regression results for the long trip dependent variables and the two-way interactions of
income and life stage, age and income, and age and life stage, respectively.
As expected, signi?cant interactions are more prevalent for the Asian destinations than for
New Zealand. The Asian destinations appeal to particular constraint groups more than
others. This might be due to distance, perceived security and language differences. New
Zealand has more consistent appeal regardless of the constraint group, however signi?cant
age and life stage interactions exist.
Research limitations/implications
This research considers the effects of important demographic and socioeconomic travel
constraints but does not consider psychological constraints. These secondary data were
collected between 2003 and 2004 and were designed by the Roy Morgan Research Centre.
However, this research does not measure a full range of constraint variables. The travel
motivations of constraint groups need to be considered in order to better understand travel
Table VI Summary of the levels of signi?cance in the binary logistic regression results of two-way interaction of age and
income for long trip dependent variable
Long trips
Interactions
Planned
places Asia
Planned
places NZ
Past places
Asia
Past places
NZ
Last stayed
places Asia
Last stayed
places NZ
Age group * HH income 0.00 0.80 0.01 0.82 0.01 0.97
Age 20 to 24 years by low income 0.02 0.36 0.00 0.21 0.00 0.57
Age 20 to 24 years by medium income 0.04 0.24 0.11 0.40 0.51 0.35
Age 25 to 34 years by low income 0.96 0.87 0.30 0.78 0.51 0.89
Age 25 to 34 years by medium income 0.31 0.71 0.27 0.95 0.30 0.68
Age 35 to 44 years by low income 0.25 0.92 0.77 0.88 0.55 0.72
Age 35 to 44 years by medium income 0.91 0.95 0.25 0.74 0.06 0.74
Age 45 to 54 years by low income 0.21 0.67 0.10 0.13 0.04 0.64
Age 45 to 54 years by medium income 0.14 0.73 0.35 0.12 0.24 0.25
Table V Summary of the levels of signi?cance in the binary logistic regression results of two-way interaction of income and
life stage for long trip dependent variable
Long trips
Interactions
Planned
places Asia
Planned places
NZ
Past places
Asia
Past places
NZ
Last stayed
places Asia
Last stayed
places NZ
HH Income * Life stage 0.00 0.90 0.00 0.94 0.00 0.81
Low income by single 0.00 0.78 0.00 0.40 0.00 0.39
Low income by couple 0.91 0.34 0.97 0.98 0.51 0.90
Medium income by single 0.00 0.73 0.01 0.68 0.01 0.81
Medium income by couple 0.33 0.73 0.60 0.91 0.55 0.96
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behavior. Investigation of psychological and ecological facilitators and constraints to travel
is needed.
Practical implications
Destination and tourism managers can utilize such results to minimize the barriers to travel
by particular groups. For example, two-income younger households with no children are
time constrained. Attractive destination packages can be designed around short get-away
themes. Speci?c tour packages and tour incentives can be designed around constraint
groups de?ned by age, income and life stage.
The similarities and differences between less and more constrained travellers, and less and
more constrained non-travellers can be a critical base for market segmentation of the
Australian market to both domestic and overseas destinations.
The results of this paper provide a pro?le of Australian travellers by age, income and life
stage. These variables can be effectively used in designing market strategies for Australian
travel markets. The ?ndings are important for those interested in travel markets. For
example, a nearby destination such as New Zealand should not necessarily utilize
demographics as a basis for segmentation. Travel marketers might focus on developing
attractive vacation packages addressing travellers’ activities, interests and opinions (AIOs),
lifestyle, and motivation for travel. Asian destinations should carefully review critical
combinations of constraint variables to identify high potential groups. Minimization of the
effects of constraints on low potential groups should also be considered. Marketers of
tourism in general may wish to consider the marketing implications of this paper. A different
marketing message should be communicated using different travel constraints and
facilitators (macrosystems and microsystems) identi?ed among the 45 constraint groups.
Less constrained groups travel more than the more constrained groups; however the paper
shows that particular segments within the constraint groups also undertake signi?cant travel.
Conclusions
This paper ?nds that age, income and life stage have signi?cant differential and interactive
effects on travel behavior. The results show that socio-demographic variables act in different
ways to constrain/free different types of travel behavior. Even the most constrained groups
undertake signi?cant travel. Many people in least constrained sectors of our society do not
travel. These phenomena need to be understood and current research is addressing these
issues.
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Corresponding author
Kenneth E. Miller can be contacted at: [email protected]
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doc_787300737.pdf